System for contraction follow-up during pregnancy

专利摘要:
The invention provides a system for monitoring contractions in pregnancy. Bio-potential signals are measured by providing at least two electrodes on the lower abdomen of a pregnant woman. The bi-potential signals are processed to derive electrohysterography (EHG) signals from it. The EHG signals are processed to derive uterine contraction signals. The uterine contraction signals are analyzed to calculate uterine contraction statistics. The system performs detection and filtering of motion artifacts. The electrodes are part of an electrode patch. A sensor module is connected to the electrode patch.
公开号:BE1024055B1
申请号:E2016/5143
申请日:2016-02-29
公开日:2017-11-09
发明作者:Julien Penders；Marco Altini；Eric Dy；Torfinn Berset
申请人:Bloom Technologies NV；
IPC主号:

专利说明:

System for contraction follow-up during pregnancy
Technical field
The present invention relates to the follow-up of a pregnancy and more particularly to a method for contraction follow-up.
State of the art
Pregnant women have numerous questions about their pregnancy, their body, and their child. One of the most stressful questions for highly pregnant women is: "Do I have contractions " The answer to this question is the most important tool for knowing when a birth is about to begin, so that a pregnant couple can prepare for the upcoming birth and eventually leave for a hospital or health institution where the birth will take place. Women can experience early contractions during the second and third trimesters, also known as Braxton Hicks contractions. Braxton Hicks contractions are an expression that the womb is slowly preparing for delivery. The feeling of a contraction is completely new and it is impossible for a woman to distinguish with certainty a contraction from other lower abdominal physiological disorders such as lower abdominal cramps or intestinal disturbances. This is certainly the case with a first pregnancy, but mothers also report this problem with a second or third pregnancy, because people often forget how a contraction feels.
In addition to pregnant women, doctors also have a major interest in contraction follow-up. History of contractions is part of the clinical examination in most countries, and a gynecologist will ask the pregnant patient if she has had contractions since her last visit, and how often. The answer to this question is inevitably careless since a woman is unable to reliably report contractions. Despite the importance of contraction follow-up outside the hospital or doctor's office, from both the consumer and doctor's perspective, there is so far no available solution for contraction follow-up outside of a controlled clinical environment. The current best alternative solution for consumers is a stopwatch, physical and digital. A stopwatch relies on personal perception and the feeling of a contraction, and is therefore intrinsically careless and does not address the problem that a woman cannot recognize or detect her contractions.
In healthcare institutions, contractions are measured with a tocograph, which measures the pressure that results from the contraction by means of a probe placed on the lower abdomen. The tocograph has various limitations. First of all, a tocograph is annoying, a woman has to put a band around her lower abdomen, and use a large pressure probe and gel. The annoyance of a tocograph limits the usability outside of a controlled clinical environment. Secondly, the positioning of the tocograph probe is important to get a reliable measurement. The consequence of this is that the tocograph must be operated by trained clinical staff and therefore cannot be used by the pregnant woman herself. Thirdly, a tocograph only measures the result of the contraction (a change in pressure on the surface of the lower abdomen), and not the actual physiological symptoms that lead to the contraction. As a result, the tocograph has shown low accuracy in characterizing contractions, in particular the intensity of the contractions.
Physiologically, the contraction with the electrical activation of uterine cells occurs, similar to the activation of muscle cells. Measuring electrical activity in the womb is a more accurate and reliable way to measure contractions, called electrohysterography (EHG). US2012 / 0150010-A1 describes a device and method for monitoring uterine activity based on EHG. However, such devices are today limited to bulky devices with electrodes and cords, operated by expert clinical personnel, and limited to the clinical environment. Attempts have already been made to improve the user comfort and size of such devices. US2007 / 0255184-A1 releases a disposable birth detection patch that uses the electromyogram signals from the uterus. However, the concept of a disposable patch that integrates all electronics will inevitably be associated with a very high cost due to the cost of the electronic components, which impedes feasibility and usability as a disposable system. Although the electrode part of the system needs to be replaced regularly for hygiene and signal quality, the electronics can be used for hundreds or thousands of recordings without having to be discarded or replaced. Furthermore, although a patch may be useful for some pregnant women, attaching an adhesive to the lower abdomen of a pregnant woman is uncomfortable because it can cause itchy sensations, irritations, allergies, or other discomfort. For such cases, considering shapes other than a patch may be beneficial.
Furthermore, the devices of US2012 / 0150010-A1 and US2007 / 0255184-A1 are limited in their functionality. These devices provide us with a measurement of the contraction signal, and do not perform any further analysis on the signal. As a result, they are of limited value to the user himself, and require the intervention of clinically experienced staff to interpret the results.
Electrodes placed on the lower abdomen of a pregnant woman are able to measure bio-potential signals that include various electrical signals generated by the body, such as the mother's electro-cardiogram (mECG), the fetal electro-cardiogram (fECG) ), as well as the uterus electrohysterogram (EHG) and uterus electromyogram (UEMG) which is equivalent to the electrical signature of the uterine muscle contraction. The electrohysterogram can be seen as a more general view of the uterine electrical activity that looks at the slower waves generated by the general activation of a larger number of uterine cells. Complementary to this, the uterus electromyogram can be seen as a more accurate picture of the uterine electrical activity, which looks at the fast electric waves generated locally by smaller groups of uterine cells. EHG signals are usually analyzed in the time domain, using RMS, linear filters or other statistics, to arrive at an estimate of a contraction signal. UEMG signals are usually analyzed in the time frequency domain using Fourier transformations, wavelet transformations, etc. EHG and UEMG are superimposed to the maternal and fetal electro-cardiogram. Signal processing techniques are also a requirement to distinguish any physiological contribution from the bio-potential signals.
All these signals are checked during long periods of time in the hospital, during delivery or in the last months of pregnancy, and must be further analyzed by a specialist. These signals are usually displayed on a screen or printed sheet of paper while the signals are being recorded.
Based on the foregoing, there is a need for a device and a method of contraction follow-up within pregnancy follow-up that can be used by pregnant women in any environment that answers the most stressful questions women have during the progress of their pregnancy, and also provides important clinical information that the doctor can use during the clinical trial.
Description of the invention
These objects have been achieved according to the embodiments of the invention.
In one embodiment, a method for contraction follow-up is provided which comprises steps of: a. Measuring bio-potential signals by providing at least two electrodes on the lower abdomen of a pregnant woman b. Processing the bio-potential signals to derive EHG signals from this c. Processing the UHG signals to derive uterine contraction signals from this, and d. Analyzing the uterus contraction signals to measure uterine contraction statistics.
This method allows the measurement of different types of bio-potential signals that carry information about the uterine activity of the pregnant woman. Every bio-potential signal can be processed and provides specific information related to the uterine activity of a pregnant woman. Combining the information corresponding to each type of bio-potential signal results in a more precise analysis of the uterine activity of the pregnant woman and allows a better and more precise diagnosis. Thanks to the uterine contraction statistics performed in step d) using different contraction signals, this diagnosis is automatically performed by the method and no specialist is required to interpret the various acquired signals. The calculated uterus statistics are used to inform the pregnant woman about the current status of her pregnancy and to warn when she needs to go to the hospital. Such a device can therefore minimize the risks of miscarriage, premature birth, and any complications related to pregnancy. Thanks to the method, the pregnant woman can always be aware of the current status of her pregnancy.
In embodiments of the invention, the processing of bio-potential signals comprises isolating and separating a portion of the bio-potential signals that is relevant to the electrical activity of the uterus.
In embodiments of the invention, step b) comprises applying a filter with a band pass from 0.3 Hz to 0.8 Hz to the biopotential signals.
In embodiments of the invention, the measured bio-potential signals include electrohysterogram signals, uterus electromyogram signals, material electrocardiogram signals, and fetus electrocardiogram signals.
In embodiments of the invention, measuring bio-potential signals includes providing at least three electrodes on the lower abdomen of the pregnant woman, the third electrode being used as a bias electrode or third leg electrode.
In embodiments of the invention, measuring bi-potential signals comprises three measurement electrodes, a reference electrode and a bias electrode.
In embodiments of the invention, the reference electrode is placed just below the navel.
In embodiments of the invention, the three measurement electrodes are positioned so that one is positioned on the right side of the reference electrode, one on the left, and one below the reference electrode.
In embodiments of the invention, the distance between the reference electrode and each measurement electrode is between 3 and 10 centimeters.
In embodiments of the invention, the electrodes are integrated into one electrode patch, wherein the step of measuring the bio-potential signals comprises providing the electrode patch on the lower abdomen of the pregnant woman.
In embodiments of the invention, the step of processing the bio-potential signals comprises at least one of the following filtering methods: time domain filtering, frequency domain filtering, time-frequency domain filtering or blind-source separation.
In embodiments of the invention, the step of processing EHG signals includes converting EHG signals into uterine contraction signals.
In embodiments of the invention, the step of processing EHG signals includes at least one of the following methods: root-mean-square (rms) calculation, average calculation, linear filters, integration operators, energy operators, or entropy operators.
In embodiments of the invention, the step of analyzing uterine contraction signals comprises deriving uterine contraction properties to calculate uterine contraction statistics.
In embodiments of the invention, the uterus contraction properties include at least one of the beginning, the end, or the amplitude of a contraction.
In embodiments of the invention, the step of analyzing the uterine contraction signals comprises determining the start of a contraction by determining the inflection point of the uterus contraction signal, verifying whether the inflection point corresponds to an increasing slope and by determining the nearest zero point of the first derivative in the uterus contraction signal for the inflection point.
In embodiments of the invention, the step of analyzing uterine contraction signals comprises determining the end of a contraction by determining the inflection point of the uterus contraction signals, verifying whether this inflection point corresponds to a descending slope and by determine the nearest zero point of the first derivative of the uterine contraction signals.
In embodiments of the invention, the step of analyzing the uterus contraction signals further comprises determining the contraction amplitude by determining the local maximum between the beginning of a contraction and the end of a contraction.
In embodiments of the invention, the step of analyzing uterine contraction signals comprises identifying patterns in the uterus contraction signals by applying a wavelet transform to the uterus contraction signals where the onset of contraction is determined by the point at which the energy of the wavelet rises above a predetermined value and the amplitude of a contraction is determined by the total energy of the wavelet transform.
In embodiments of the invention, the step of analyzing the uterus contraction signals comprises linking uterus contraction signals to template signals stored in a database.
In embodiments of the invention, the database user is specific.
In embodiments of the invention, the uterus contraction statistics include at least one of frequency, duration, or intensity of a contraction.
In embodiments of the invention, the step of measuring bio-potential signals comprises receiving bio-potential signals, and conditioning and amplifying the received bio-potential signals.
In embodiments of the invention, the step of measuring bio-potential signals comprises filtering artifacts.
In embodiments of the invention, filtering artifacts includes measuring a motion artifact signal in parallel with the bi-potential signals.
In embodiments of the invention, the motion artifact signal is measured by an accelerometer attached to at least two electrodes.
In embodiments of the invention, the motion artifact signal is a contact impedance measured by using at least two electrodes.
In embodiments of the invention, the step of filtering artifacts includes receiving measured motion artifact data.
In embodiments of the invention, the step of analyzing the uterus contraction signals comprises deriving uterus contraction properties from the uterus contraction signals, detecting contractions, and calculating uterine contraction statistics.
In embodiments of the invention, the method further comprises (e) processing the bio-potential signals to derive uterine electromyogram (UEMG) signals. (f) processing the UEMG signals to derive UEMG markers, and (g) classifying the bio-potential signals into contraction types based on the UEMG markers.
In embodiments of the invention, the step of processing UEMG signals includes deriving UEMG properties to derive UEMG markers.
In embodiments of the invention, at least one of the uterine contraction statistics from step (d) is used as input for processing UEMG signals in step (f).
In embodiments of the invention, the method further comprises (h) identifying bio-processing signals that represent a birth.
In embodiments of the invention, the step of identifying bio-processing signals representing a birth uses at least one of the UC statistics of step (d) or a contraction type of step (g).
In embodiments of the invention, the method further comprises (i) processing the bio-potential signals to derive a maternal electro-cardiogram (mECG) signal, (j) processing the mECG signal to the heartbeat of the mother (maternai heart rate, mHR).
In embodiments of the invention, the step of processing the mECG signals includes analyzing the mECG signals to derive mECG R-waves.
In embodiments of the invention, the method further comprises (k) processing the mHR to derive the stress level from the mother.
In embodiments of the invention, the method further comprises (1) processing the bio-potential signals to derive fetal electro-cardiogram (fECG) signals, (j) processing the fECG signals at a fetal heart rate (fHR ) to distract.
In embodiments of the invention, the processing process to derive fECG signals in step (I) uses the mECG signals from step (i) as input to filter the mECG signals from the other fECG signals.
In embodiments of the invention, processing the fECG signals includes determining the morphology of the fECG signals.
In embodiments of the invention, the method further comprises the step of simultaneously visualizing the UC signals and the fHR signals to determine changes in the fHR during a contraction.
In embodiments of the invention, the method further comprises measuring activity of the mother by means of an activity sensor.
In embodiments of the invention, the method further comprises measuring fetal activity by means of an accelerometer placed on the lower abdomen of the pregnant woman.
In embodiments of the invention, the method further comprises providing information to the pregnant woman based on at least one of the UC statistics of step (d) or a contraction type of step (g).
In one embodiment, a contraction tracking device is provided that includes an electrode patch that includes at least two electrodes, including a measurement electrode and a reference electrode, and a sensor module configured to be connected to the electrode patch. The sensor module comprises a signal acquisition module, a signal processing module, a power management module, a sensor module, and at least one of the memory module or a data transmission module.
As long as the electrodes are processed in the electrode patch, there is no risk of a woman placing the different electrodes incorrectly since their mutual positioning is already correct on the electrode patch. The use of an electrode patch also improves the experience and use of contraction follow-up, since it does not require different electrodes attached to the lower abdomen, but only requires the attachment of a single electrode patch. This electrode patch can also be positioned on the lower abdomen of a woman. It can then be attached with an adhesive layer or processed in any garment or textile. The woman can also continue her usual activities while she wears it and does not have to lie down like with most current contraction followers used in the medical environment. The sensor module is configured to receive all the biopotential signals through the signal acquisition module. The signal processing module is responsible for converting these signals into data that can be understood by the user, all this data is transferred to a portable device of the user thanks to the data transmission module or can be kept in the memory of the device itself. All these operations are coordinated by the sensor control module. The sensor module allows the user to visualize all signals and information related to the uterus activity on a portable device and to be informed accordingly.
In embodiments of the invention, the electrode patch comprises a sensor module connection area to connect the sensor module to the electrode patch.
In embodiments of the invention, the electrode patch is disposable.
In embodiments of the invention, the electrode patch and the sensor module are in a garment or belt.
In embodiments of the invention, the electrode patch comprises an adhesive layer to be attached to the user's body.
In embodiments of the invention, the electrode patch comprises electrode cords.
In embodiments of the invention, the electrode patch is connected to the sensor module through a magnetic connection, the magnetic connection being configured to provide electrical contact between the sensor module and the electrode patch when connected.
In embodiments of the invention, the electrode patch is connected to the sensor module through a mechanical connection, the mechanical connection being configured to provide electrical contact between the sensor module and the electrode patch when connected.
In embodiments of the invention, the electrode patch further comprises a bias electrode.
In embodiments of the invention, the bias electrode is substantially located in the center of the electrode patch, and wherein the measurement electrode is located on one side of the bias electrode and the reference electrode on the other side than the one side relative to the bias electrode.
In embodiments of the invention, the electrode patch comprises a second measurement electrode, the first and second measurement electrodes being located at the two extremes of the electrode patch, the reference electrode being located substantially in the center of the electrode patch and the bias electrode is located between one of the measurement electrodes and the reference electrode.
In embodiments of the invention, the electrode patch comprises a back electrode which, in use, is positioned on the back of a pregnant woman.
In embodiments of the invention, the electrode patch further comprises a third measurement electrode, the first and second measurement electrodes and the reference electrode being positioned substantially on a line, the third measurement electrode being positioned below the reference electrode substantially perpendicular to the line, and wherein the bias electrode is located between a measurement electrode and the reference electrode.
In embodiments of the invention, the distance between the measurement electrode and the reference electrode is between three and ten centimeters.
In embodiments of the invention, the signal processing module is configured to perform any of the provided method embodiments.
In embodiments of the invention, the signal acquisition module comprises a conditioning module.
In embodiments of the invention, the signal acquisition module comprises an amplification module.
In embodiments of the invention, the signal acquisition module comprises an analog filter module.
In embodiments of the invention, the signal acquisition module comprises an analog-to-digital conversion module.
In embodiments of the invention, the memory module is configured to store data generated by the signal processing module.
In embodiments of the invention, the data transmission module is configured to send signals generated by the signal processing module to a user device.
In embodiments of the invention, the energy management module is configured to supply power to the contraction tracking device.
In embodiments of the invention, the sensor module further comprises an inertial motion sensing module.
In embodiments of the invention, the inertial motion sensing module comprises at least one of a one-axis accelerometer, a two-axis accelerometer, or a three-axis accelerometer.
In embodiments of the invention, the inertial motion sensing module comprises at least one of a single-axis gyroscope, a two-axis gyroscope, or a three-axis gyroscope.
In embodiments of the invention, the inertial motion sensing module comprises at least one of a one-axis magnetometer, a two-axis magnetometer, or a three-axis magnetometer.
In embodiments of the invention, the sensor module further comprises a contact impedance measurement module.
In embodiments of the invention, the sensor module further comprises a user interface module.
In embodiments of the invention, the user interface module comprises at least one LED.
In embodiments of the invention, the user interface module comprises at least one of a buzzer, a vibrating element, a speaker, or a display.
Brief description of the drawings
The invention will be explained in more detail below with reference to the following description and the accompanying drawings. FIG. 1 is an exemplary top-level flow diagram illustrating an embodiment of the method for measuring contractions based on bi-potential signals. FIG. 2A is an exemplary diagram illustrating the position of the electrodes on the lower abdomen of a woman. FIG. 2B is an example representation of a uterine contraction, uterine contraction properties and uterine contraction statistics. FIG. 3A is an exemplary flow diagram illustrating another alternative embodiment of the method for detecting uterine contractions of FIG. FIG. 3B is an exemplary flow diagram illustrating an alternative embodiment of the method for detecting uterine contractions of FIG. 3A. FIG. 4 is an exemplary flow diagram illustrating yet another alternative embodiment of the method for detecting uterine contractions of FIG. 1. FIG. 5A is an exemplary flow diagram illustrating yet another alternative embodiment of the method for monitoring uterine contractions of FIG. 1. FIG. 5B is an exemplary flow diagram illustrating an alternative embodiment of the method for monitoring uterine contractions of FIG. 5A. FIG. 6 is an exemplary flow diagram illustrating an alternative embodiment of the method for monitoring uterine contractions of FIG. 5A or FIG. 5B. FIG. 7 is an exemplary flow diagram illustrating an alternative embodiment of the method for monitoring uterine contractions of FIG. 6. FIG. 8 is an exemplary flow diagram illustrating yet another alternative embodiment of the method for monitoring uterine contractions of FIG. 1. FIG. 9A is an exemplary flow diagram illustrating yet another alternative embodiment of the method for monitoring uterine contractions of FIG 1. FIG. 9B is an exemplary flow diagram that an alternative
illustrates embodiment of the method for monitoring uterine contractions of FIG. 9A FIG. 9C is an exemplary flow diagram illustrating an alternative embodiment of the method for monitoring uterine contractions of FIG. 9A or FIG. 9B. FIG. 9D is an exemplary flow diagram illustrating another alternative embodiment of the method for monitoring uterine contractions of FIG. 9A or FIG. 9B. FIG. 10 is an exemplary flow diagram illustrating another alternative embodiment of the method for monitoring uterine contractions of FIG. 9A or FIG. 9B. FIG. 11 is an exemplary flow diagram illustrating yet another alternative embodiment of the method for monitoring uterine contractions of FIG. 1. FIG. 12 is an exemplary flow diagram illustrating yet another alternative embodiment of the method for monitoring uterine contractions of FIG. 1. FIG. 13 shows an exemplary illustration of an embodiment of the device for contraction follow-up. FIG. 14 shows an exemplary illustration of another embodiment of the contraction follow-up device. FIG. 15 shows an exemplary illustration of another embodiment of the contraction follow-up device of FIG. 13. FIG. 16 shows an exemplary illustration of another embodiment of the contraction follow-up device of FIG. 13. FIG. 17 shows an exemplary illustration of another embodiment of the contraction follow-up device of FIG. 13. FIG. 18 shows an exemplary illustration of another embodiment of the contraction follow-up device of FIG. 13. FIG. 19 is an exemplary block diagram of an embodiment of the sensor module of FIG. 13-18. FIG. 20 is an exemplary block diagram of an alternative embodiment of the sensor module of FIG. 13-18. FIG. 21 is an exemplary block diagram of yet an alternate embodiment of the sensor module of FIG. 13-18. FIG. 22 is an exemplary block diagram of yet another alternative embodiment of the sensor module of FIG. 13-18. FIG. 23 shows an exemplary illustration of yet another embodiment of the contraction follow-up device of FIG. 14, in which the contraction follow-up device communicates with users' personal devices.
Embodiments of the invention
The present invention will be described with respect to specific embodiments and in connection with certain drawings, but the invention is not limited thereto but only by the claims. The described drawings are only schematic and are not limitative. In the drawings, the size of some elements is sometimes exaggerated and not drawn to scale for illustrative purposes. The dimensions and the relative dimensions necessarily refer to actual reductions to practice of the invention.
Furthermore, the terms first, second, third, and similar in the description and in the claims, are used to distinguish between similar elements and not necessarily to describe a sequential or chronological order. The terms are interchangeable under suitable conditions and the embodiments of the invention may function in sequences other than described or illustrated herein.
Incidentally, the terms top, bottom, over, under and similar in the description and claims are used for descriptive purposes and not necessarily for describing relative positions. The terms so used are interchangeable under suitable conditions and the embodiments of the invention described herein may function in orientations other than described or illustrated herein.
Furthermore, the various embodiments, although listed as "preferred", are constructed as exemplary ways in which the invention can be applied instead of limiting the room of movement of the invention.
The term "include" used in the claims is not to be interpreted as being limited to the elements in the steps listed thereafter, it does not exclude other elements or steps. It should be interpreted as specifying the presence of the explained properties, wholes, steps or components as referred to, but it does not exclude the presence or addition of one or more other properties, integers, steps or components, or groups thereof. Thus, the freedom of movement of the expression "a device comprising A and B" should not be limited to devices comprising only components A and B, instead of concerning the present invention, the only enumerated components of the device are A and B, and further, the conclusion must be interpreted as including equivalents of these components.
Given that current solutions for contraction follow-up are only available to healthcare professionals and for use in a controlled healthcare environment, are limited in their functionality, and are limited in their portability and accuracy, a method and equipment for ambulatory contraction follow-up may prove desirable for allowing a woman to monitor her contractions at any time and in any environment, to gain new insights into how other health parameters can affect her contractions, or to share this information with their partner, family, friends and health professionals during or between visits.
At least two cutaneous electrodes including a measurement electrode and a reference electrode, provided with a portable system including an electrode patch and a sensor module, are placed on the lower abdomen of a pregnant woman, measuring bio-potential (EXG) signals which include electrohysterogram (EHG) , uterine electromyogram (UEMG), maternal electrocardiogram (mECG), fetal electrocardiogram (fECG), etc. signals. This method allows the derivation and isolation of the necessary signals to measure the measurement of the uterine contractions. The combination of the different signals promotes the precision of the analysis of the uterine contractions and all its implications for the mother and fetus.
Furthermore, the combination of the different signals provides new insights into the relationship between the mother's behavior and her contractions. The method is extremely robust and requires no professional to place, control or analyze the obtained signals. Furthermore, the method is suitable for use directly by the pregnant woman.
The method advantageously monitors contractions based on bio-potential (EXG) signals measured on the abdomen of a pregnant woman. This can be achieved, according to one of the embodiments released herein, in the method 100 for monitoring uterine contractions of EXG signals as illustrated in FIG. 1. As shown in FIG. 1, the method 100 for monitoring uterine contractions may include:
Measuring, at 110, of bio-potential (EXG) signals, including EHG signals,
Process, at 120, of EXG signals to derive electrohysterogram (EHG) signals
Process, at 130, of EHG signals to derive uterine contraction (UC) signals
Analyze, at 140, of UC signals to calculate relevant UC statistics.
The measurement, at 110, of EXG signals can be achieved by means of at least two electrodes, which makes at least one channel EXG signal possible. In this configuration, one electrode may be referred to as the measurement electrode, while the second electrode may be referred to as the reference electrode. In one embodiment, the measurement of EXG signals can be obtained by using a third electrode, used as a bias electrode or as a right-leg drive electrode, for the primary purpose of reducing the noise of the EXG measurement. In an alternative embodiment, the measurement of EXG signals can be achieved by the use of additional electrodes, which allow the measurement of multiple channel EXG signals. The different electrodes can be placed at different places on the lower abdomen, and advantageously provide a multi-dimensional measurement of uterine electrical activity.
In a preferred embodiment, illustrated in FIG. 2A, uses the method 100 by contraction monitoring of FIG. 1 three measurement electrodes (3004, 3005, 3006), a reference electrode 3002 and a bias electrode 3003. The reference electrode 3002 is positioned just below the navel 3001. The three measurement electrodes (3004, 3005, 3006) are positioned respectively on the right, on the left, and below the reference electrode 3002, as illustrated in FIG. 2A. Preferably, the distance between the reference electrode 3002 and each measurement electrode (3004, 3005, 3006) is between three and ten centimeters. The bias or right leg drive electrode 3003 can be positioned anywhere on the lower abdomen, but not too close to the other electrodes.
Preferably and most advantageously, all electrodes can be integrated into an electrode patch. The electrode patch can significantly enhance reliability, as well as the experience and use of method 100 for contraction follow-up of FIG. 1. The use of an electrode patch promotes the reliability of contraction follow-up, since it is impossible for the user to place the different electrodes incorrectly in relation to each other because they are always in the same relative position. The use of an electrode patch promotes the experience and ease of use of contraction follow-up, since it does not require placement of different electrodes on the lower abdomen, but only the attachment of a single electrode patch.
Referring again to Figure 1, the processing, at 120, of EXG signals to derive EHG signals may include isolating the EXG signals to separate and exclude the part of the EXG signals relevant to the electrical activity of the uterus of the part of the EXG that is related to other physiological phenomena, noice, artifacts and any other contributions. The processing, at 120, of EXG signals to derive EHG signals can be achieved through the use of signal processing techniques that include but are not limited to time-domain filtering, frequency-domain filtering, time-frequency-domain filtering and / or blind-source separation. For example, and because physiology teaches us that EHG signals are known to have a frequency content within the 0.3 to 0.8 Hz frequency band, any EHG signal can be derived from the corresponding EXG signal by applying a filter with a band pass of 0.3 Hz to 0.8 Hz. In another example, the multiple EXG channels are combined and processed using independent component analysis or other blind source separation techniques to separate components of EXG signals with different variations. The components with the most variation in the low frequency band can then be retained as the EHG signals.
Processing, at 130, EHG signals to derive UC signals may include converting EHG signals to UC signals. The processing, at 130, of EHG signals to derive UC signals can be achieved through the use of signal processing techniques that include but are not limited to root-mean square, average calculation, linear filters, integration operators, energy operators or entropy operators .
Analyzing, at 140, UC signals to calculate relevant UC statistics, may further analyze the UC signals to derive relevant UC properties and calculate relevant UC statistics. The UC signal is usually not intelligible for a person without a clinical background. Therefore, although the UC signal can be a great source of information for a clinically trained expert, it gives little value to the non-clinically qualified user, like most pregnant women. Analyzing, at 140, UC signals to calculate relevant UC statistics and analyzing the relevant UC signals to provide a number of features that are understandable to the common user. Relevant UC features can include but are not limited to the beginning, end, amplitude of a contraction. In one embodiment, the tracing of the onset of a contraction can be achieved by tracing the inflection point in the UC signal, checking that this inflection point corresponds to a rising slope, and then finding the nearest zero point of the first derivative in the UC signal for the inflection point. The zero point of the first derivative can be used as an estimate of the start of a contraction. Similarly, the end of a contraction can be traced by locating the inflection point in the UC signal, checking that this inflection point corresponds to a descending slope, and then finding the nearest zero point of the first derivative of the UC signal after the inflection point. That zero point of the first derivative can be used as an estimate of the end of a contraction. Then the local maximum between the beginning of a contraction and the end can be used as the measurement of the contraction amplitude. In another example, the amplitude of the UC signal between the beginning and the end of a contraction can be used as a measurement of the contraction amplitude. In another embodiment, a wavelet transform can be applied to the UC signal to detect patterns in the UC signal that correspond to a contraction. The start (or the end) of the contraction can then be defined as the point at which the energy of the wavelet transform rises higher (or falls lower) than a certain threshold. The amplitude of the contraction can then be calculated as the total energy of the wavelet transform between these two points, or as the local maximum in the time domain between the beginning and the end of the contraction. In yet another embodiment, template matching can be used to recognize individual contractions. For example, a typical contraction template can be built from a database of contraction recordings. This database may or may not be user-specific. Then the UC signal can be associated with the contraction template, and the maximums in the cross-correlation function can be considered as being the contraction. Start, end and amplitude can then be calculated as described above. Relevant UC statistics may include but are not limited to frequency, duration, or intensity of contractions. Frequency and duration can be calculated directly from the beginning and the end of all contractions. The intensity of contractions can be calculated as the amplitude of the contraction. FIG. 2B shows an example of UC properties and statistics derived from a UC signal. FIG. 3A shows another alternative embodiment of the method 100 for follow-up on uterine contractions of FIG. 1, in which measuring, at 110, of EXG signals may include: receiving, at 111, of EXG signals and obtaining, at 112, of EXG signals. The acquisition, at 112, of EXG signals can be achieved with electronics for conditioning and amplifying the EXG signals, and for converting the analog EXG signals into digital EXG signals (not shown). EXG signals may be corrupted by motion artifacts, resulting in noise and interference in the signal that may affect its interpretation. Motion artifacts are especially present in the case of ambulatory environments. Despite these artifacts, it is important to accurately and accurately measure the EXG signals. Preferably, the method comprises an automatic identification of motion artifacts. In fact, the presence of artifacts can lead to misinterpretations of the signal. Processing techniques to detect motion artifacts are therefore required to be able to identify EXG erasures that have been corrupted with artifact. In a preferred and more advanced embodiment, processing techniques to remove such artifacts may be even more beneficial since the removal of motion artifacts from the EXG can avoid the need to exclude erasures from the measurement. FIG. 3B shows an alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 3A, wherein measuring, at 110, of EXG signals may further include motion artifact filtering, at 113. Motion artifact filtering, at 113, may be done in the analog domain for amplifying the EXG signals, and / or in the digital domain. Motion artifact filtering, at 113, conveniently detects and removes artifacts from the EXG signals, to increase the quality and signal-to-noise ratio of the EXG. Motion artifact filtering, at 113, can be achieved through a variety of signal processing techniques, including but not limited to: bandpass filters, linear filters, adaptive filters, wavelet filters, or blind source separation techniques.
Alternatively, motion artifact filtering, at 113, can be achieved using an additional motion artifact signal that is measured in parallel with the EXG signals and in particular carries information about the artifacts. For example, the motion artifact signal can be measured with an accelerometer attached to the electrode patch. In another example, the motion artifact signal may be the contact impurity measured by the same electrodes as those used to measure EXG signals. The motion artifact signal can be used as an input for the motion artifact filter. For example, the motion artifact signal can be used as input for an adaptive filter that represents an estimate of the noise. The adaptive filter, at 113, can then function to remove the noise estimate from the EXG signals, leading to more pure and more accurate signals. FIG. 3C shows an alternative embodiment of the method 100 for follow-up on uterine contractions of FIG. 3B, wherein motion artifact measurement, at 150, is used as an input for motion artifact filtering, at 113. FIG. 4 shows another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1, wherein analyzing, to 140, UC signals to calculate relevant UC statistics preferably includes deducing, to 141, UC properties, detecting, to 142, contractions, and calculating, to 143 UC statistics. Deriving, at 141, UC properties may include processing of UC signals to lead to a group of properties that characterize the UC signals. Examples of properties may include but are not limited to time-domain properties (zeros of the first derivative, inflection points, local minima, local maxima), frequency-domain properties, or time-frequency properties. The detection of contractions, at 142, can be performed by using a group of thresholds and / or conditions on selected UC properties. The thresholds or conditions can be set manually, or can adjust automatically based on the measured signal. For example, in the time domain, a contraction can be detected when identifying the following specific sequence: an inflection point with a rising slope, followed by a local maximum, and followed by an inflection point with a falling slope. In another example, in the time-frequency domain, a contraction can be detected when the energy of the wavelet transform into a predetermined band corresponding to 0.3-0.8 Hz exceeds a certain threshold. The calculation, at 143, of statistics can be achieved by further analyzing the UC data for the contractions detected at 142. For example, statistics such as average duration of contractions, time between contractions, or average amplitude can be calculated based on the properties derived to 141 for contractions detected to 142. EHG signals carry information about the UC signals. However, EHG signals are limited in their frequency content. In other words, EHG signals provide a high-level picture of the electrical activity of the womb. In addition to the EHG, the EXG signals include much more information and that information fits the electrical activity of the uterus. Physiology teaches us that a contraction can be seen as the result of the general activation of thousands of uterine muscle cells. The speed at which the uterine contraction cells fire, the pattern in which they fire, and the spatial distribution of the firing, all contain important information about the electrical activity of the uterus, which can be referred to as the fine information of the uterine electrical activity. An additional advantage is that the fine information gives a more detailed view of the contractions, and can be used to gain additional knowledge about the contraction. The fine information can be used, for example, to distinguish between different types of contractions, or to give a better insight as to whether a contraction can lead to a birth or not. The fine information about the womb electrical activity is not included in the EHG signals. This information can be derived, in addition to the EHG signal, from a uterus electromyogram signal, or UEMG signal. FIG. 5A shows yet another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1, wherein the method 100 for monitoring uterine contractions may further comprise:
Processing, at 210, of EHG signals to derive uterine electromyogram (UEMG) signals
The processing, at 220, of UEMG signals to derive UEMG markers
The classification, to 230, of contraction type based on the UEMG markers.
The processing, at 210, of EXG signals to derive UEMG signals may consist of isolating from the EXG signals, the part of the EXG signals that is relevant to the uterus electromyogram, and separating the part of the EXG that is related is due to other physiological phenomena, noise, artifacts and some other contributions. The processing, at 210, of EXG signals to derive UEMG signals can be achieved through the use of signal processing techniques including but not limited to time-domain filtering, frequency-domain filtering, time-frequency-domain filtering and / or blind source separation. It is noted that UEMG signals and EHG signals do not have to be mutually exclusive. In other words, the UEMG signals and the EHG signals can partly overlap.
The processing, at 220, of UEMG signals to derive UEMG markers can consist of analyzing the UEMG signals to derive relevant UEMG properties that can be considered as UEMG markers. Examples of UEMG markers may include but are not limited to UEMG statistical properties (average, percent, standard difference, kurtosis or any other statistical methods), energy spectrum properties (total bandwidth energy, peak current, average current, current and certain frequency bands ), entropy properties, spatial propagation properties (laplacian, gradient, and higher order propagation properties), etc. The UEMG markers provide a quantification of the fine-grained details of the womb electrical activity. These markers can then be used to distinguish and classify different types of contractions.
The classification, to 230, of contraction types based on UEMG markers can consist of characterizing the specific type of a contraction based on the UEMG markers.
The classification, at 230, can be done by classification techniques under succession or without follow-up. Examples of classification techniques may include but are not limited to: decision trees, bayesian networks, artificial neural networks, support vector machine, Markov chains, hierarchical models, etc. In a further embodiment, the classification to 230 may consist of distinguishing from a Braxton Hicks contraction from a real birth contraction.
To improve the robustness of the method, FIG. 5B an alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1 and FIG. 5A, wherein the output of analyzing, to 100, of UC signals is used in processing, to 220, of UEMG signals to derive UEMG markers. For example, contractions detected by the analysis of UC signals can be used to define the retrieval of the UEMG signals from which the UEMG markers can be derived. The processing, at 220, of the UEMG signals to derive the UEMG markers can then provide a finer and more detailed analysis of the contraction, in which the finer time, frequency and time-frequency properties can be derived to provide a complete characterization of the provide for contraction. An additional advantage is that the finer characterization can either provide new information and / or can be used to promote the accuracy and robustness of classification at 230. FIG. 6 shows another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 5A or FIG. 5B, wherein the method 100 for monitoring uterine contractions may further include detecting a delivery, at 240.
Detecting a delivery, at 240, can be used to detect the UC statistics obtained by analyzing, at 140, UC signals and / or the contraction type obtained by classifying, at 230, contractions to see whether a birth is about to start. Providing an estimate of the gestational age may be available, tracing a birth can also be used to detect the start of an early birth, defined as giving birth for 37 weeks of gestational age.
Tracing a birth, to 240, can be achieved using analytical methods that include but are not limited to decision trees, conditional logic, support vector machines, artificial neural networks, bayesian networks, Markov chains, hierarchical model, etc. In one For example, the tracing of a birth, up to 240, can be implemented according to the usual pregnancy practice such as the “411” rule, according to which a pregnant woman should go to the hospital if she has contractions at least every four minutes, of at least one minute duration and for at least an hour. Preferably, the "411" rule can be combined with an estimate of the type of contractions, to ensure that the detected contractions are true birth contractions and not Braxton Hicks contractions or other physiological phenomena. FIG. 7 illustrates an exemplary embodiment of tracing a delivery, at 240, of FIG. 6, in which tracing a birth, at 240, can be achieved using a decision tree based on the UC statistics and contraction type.
Turning to FIG. 7, the decision tree can use the UC statistics as input to display the status of the birth, namely "birth" or "no birth". As illustrated in FIG. 7, tracing a birth, to 240, may include:
Checking, at 241, whether the contraction type corresponds to real birth contractions
Checking, to 242, whether the contractions are at least X minutes long
Checking, to 243, whether the contractions are separated by at least Y minutes
Checking, to 242, whether this has happened for at least Z hour
In a given example, X = 1, Y = 4 and Z = 1, this applies the "411" rule that is known by pregnancy care experts.
It is known that during pregnancy contractions prepare the body for delivery. However, painful and frequent contractions can be problematic as they can lead to premature birth with dramatic consequences for the baby. There is today a very limited knowledge of the relationship between a pregnant woman's lifestyle and the number, frequency, duration, and intensity of her contractions. Something different and more generally expressed, there is limited information about the relationship between lifestyle and contraction profiles. An important lifestyle habit that is known to have an impact on the outcome of a pregnancy is stress from the mother. Maternal stress can be deduced from heart rate variability. Information about the heart rate variability is carried in the EXG signals measured according to the method 100 for contraction monitoring of FIG. 1. Therefore, by the method 100 for contraction follow-up of FIG. 1, to measure favorably the maternal stress and to relate it to the contraction profile. FIG. 8 shows yet another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1, wherein the method 100 for monitoring uterine contractions can further include:
Processing, at 310, of EXG signals to derive ECG from the mother (mECG) signals
Processing, at 320, mECG signals to derive the mother's heartbeat (mHR)
Processing, at 330, mHR and mHRV to derive the stress level of the mother
Relating, to 340, contractions with maternal stress level.
The processing, at 310, of EXG signals to derive mECG signals may consist of isolating from the EXG signals the part of the EXG signals relevant to the mother's electrocardiogram, and separating them from the part of the EXG that is related to other physiological phenomena, noise, artifacts and any other additions.
The processing, at 310, of EXG signals to derive mECG signals can be achieved by using signal processing techniques including but not limited to time-domain filtering, frequency-domain filtering, time-frequency-domain filtering, and / or blind source separation.
The processing, at 320, of mECG signals to derive mHR and MHRV may consist of analyzing the mECG signals to derive mECG specific characteristics, in particular the mECG R-wave (also called the R-peak), of which RR intervals, mHR and mHRV can be calculated simultaneously. Examples of signal processing techniques to detect the R-wave from the mECG signals may include but are not limited to: average calculation, derivation filters, linear filters, bandpass filters, continuous wavelet, discrete wavelet, template matching, etc. Simultaneously mHR can be calculated from the distance between two consecutive R-waves. mHRV can then be calculated from the variations in heart rate. mHRV can be calculated by using time-based or frequency-based statistical properties. Preferably, the R-waves can be detected by using continuous wavelet transform, for example by using a Mexican Hat or Daubechies wavelet. The mECG signals can then be processed with a wavelet filter, and a threshold is applied to the output of the wavelet filter to filter possible R ~ wave candidates. All R-wave candidates are then filtered and only the one with the highest energy within a certain time frame, for example 1 second, is held as R-wave.
The processing, at 330, of mHR and mHRV to divert maternal stress can be achieved by combining mHR, mHRV, and / or trends and abnormalities in mHR or mHRV to obtain a measurement of the autonomic nervous system associated is stressed. In a further embodiment (not shown), processing 330, mHR and mHRV to derive stress from the mother using context information coming from a user device, such as a smartphone, to ensure the accuracy and reliability of the stress to promote estimation. Accuracy can be promoted by identifying context in which mHR and mHRV are most likely associated with the activation of the autonomic nervous system, as opposed to an increase in physical activity, for example. Context can be obtained from user activity and / or user daily routines. Daily routines can be estimated from the user specific activity and / or location.
Linking, at 340, contractions with maternal stress level can be achieved by looking at the relationship between maternal stress on the one hand, and UC statistics and / or contraction type on the other. Linking, to 340, contractions to the maternal stress level can provide new insights on how maternal stress can affect contractions. For example, a woman may be able to discover that she has more contractions when her stress level is higher. FIG. 9A shows yet another alternative embodiments of the method 10 for monitoring uterine contractions of FIG. 1, wherein the method 100 for monitoring uterine contractions may further comprise:
Processing, at 410, of EXG signals to derive fetal ECG (fECG) signals
Processing, to 420, fECG signals to derive fetal heart rate (f H R) and fetal heart rate variability (f H RV)
The processing, at 410, of EXG signals to derive fECG signals may consist of isolating the EXG signals from the part of the EXG signals that is relevant to the fetal electrocardiogram, and separating from the part of the EXG that is related is due to other physiological phenomena, noise, artifacts and other additives. The processing, at 410, of EXG signals to derive fECG signals can be achieved by using signal processing techniques that include but are not limited to template matching, average calculation, time-domain filtering, frequency-domain filtering, time-frequency- domain filtering and / or blind source separation.
The processing, at 420, of fECG signals to derive fHR and fHRV may consist of analyzing the fECG signals to derive fECG fiducial points, in particular the fECG R-wave (also called the R-peak), whose RR intervals, fHR and fHRV, can be calculated simultaneously. Examples of signal processing techniques to detect the R-wave from the fECG filters can be included but are not limited to: average calculation, derivation filters, linear filters, bandpass filters, continuous wavelet, discrete wavelet, template matching, etc. Simultaneously, fHR can be calculated from the distance between two consecutive R-waves. FHRV can then be calculated from the variations in heart rate. fHRV can be calculated by using time-based or frequency-based statistical properties. FIG. 9B shows an alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 9A, wherein processing, to 410, of EXG signals to derive fetal ECG (fECG) signals can use the mECG as input. The mECG signals can be used in processing the EXG signals to filter the mECG signals from the fECG signals, thereby promoting the signal-to-noise ratio on the fECG signals. Preferably, the processing, at 410, of EXG signals to derive fECG signals includes adaptive filtering, in which the EXG signals and the mECG signals can be used as an input for the adaptive filter. The mECG signals can be used as input for an adaptive filter that represents an estimate of the noise on the fECG signals. The adaptive filter can then function to remove the noise estimate from the EXG signals, or in other words, to widen the mECG from the EXG signals, so as to provide a cleaner version of the EXG signals with a reduced addition of the mECG signals. The cleaner EXG signals can then be further processed using the method of FIG. 9A. FIG. 9C shows an alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 9A or FIG. 9B, wherein method 100 for monitoring uterine contractions may further include processing, to 430, of fECG to derive the position of the fetus. The processing, at 430, of fECG to derive the position of the fetus can be favorably done by exploiting the fact that the morphology of the fECG is influenced by the relative position of the fetus of the measurement electrodes. FIG. 9D shows an alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 9A or FIG. 9B, wherein the method 100 for monitoring uterine contractions may further include processing, at 440, of fECG to divert the movement of the fetus. The processing, at 440, to derive the movement of the fetus can be favorably done by exploiting the fact that the morphology of the fECG is influenced by the relative movement of the fetus of the measurement electrodes. FIG. 10 shows yet another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 9A or FIG. 9B, wherein the method 100 for monitoring uterine contractions further relates to 450, to contractions with the fHR or fHRV. Relating to 450 here can correspond to visualizing two synchronized graphs positioned on top of each other. The changes in fHR during a contraction can provide insight into the fetus's reaction to the contractions and can therefore carry important information about the health status of the fetus. Additionally, it is advantageous that the method 100 for monitoring uterine contractions of FIG. 10 is able to extract from the measurement EXG signals, contractions and fHR, and thus all information needed to perform a clinical standard non-stress test with an extreme simplicity of use that can be applied in any environment, also at home . Preferably, this information can then be shared remotely with clinically trained staff who can interpret the data. FIG. 11 shows yet another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1, wherein the method 100 for monitoring uterine contractions may further comprise:
Measuring, to 510, of activity of the mother
Relating, to 520, contractions with activity of the mother
Measuring, at 510, activity of the mother can be achieved by the use of an activity sensor embedded in a smartphone, which uses a dedicated activity tracker or an activity sensor embedded in the contraction monitor. Activity measurements may include but are not limited to: steps, activity drive, activity types, time spent in different activity types, energy consumption, calories burned, duration of sleep, quality of sleep. The activity sensor can track activity of the mother over time, for specific recordings, or continuously and 24/7.
Linking, to 520, contractions with activity of the mother can be achieved by looking at the relationship between the level of activity of the mother on one side, and UC statistics and / or contraction type on the other. Associating, to 520, contractions with maternal activity can provide new insights on how maternal activity can affect contractions. For example, a woman may be able to discover that she has more contractions if she is more active. FIG. 12 shows yet another alternative embodiment of the method 100 for monitoring uterine contractions of FIG. 1, wherein the method 100 for monitoring uterine contractions may further comprise:
Measuring, at 610, fetal activity
Relating, to 620, contractions with fetal activity
Measurement, at 610, of fetal activity can be achieved using the method of FIG. 9D. Alternatively or additionally, fetal activity can be measured, at 610, using an accelerometer positioned on a woman's lower abdomen. Additionally, it is beneficial that fetal activity can be measured, at 610, by using a combination of the method of FIG. 9D and accelerometers. This is beneficial because the method of FIG. 9D may be more accurate in detecting general movement of the fetus, while accelerometer-based fetal activity measurements may be more accurate for localized fetus movements such as kicking. Combining both methods can therefore allow for the detection of both general and local movements of the fetus. Fetal activity can be measured, at 610, during specific recording sessions, or continuously and 27/7. Linking, to 620, contractions with fetal activity can be achieved by viewing the relationship between fetal activity on one side, and UC statistics and / or contraction type on the other. Linking, at 620, contractions to maternal activity can provide favorable new insights into how fetal activity is related to contractions. For example, a woman may be able to discover that her baby is less active when she has contractions.
In yet another alternative embodiment (not shown) of the method 100 for monitoring uterine contractions of FIG. 5A, FIG. 5B, FIG 6, FIG. 8, FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, FIG. 10, FIG. 11 or FIG. 12, wherein the method 100 for monitoring uterine contractions may further include providing user feedback. Providing user feedback can provide recommendations or suggestions to the pregnant woman based on UC statistics determined by analyzing, 140, UC signals and / or the contraction type determined by classifying, at 230, contractions. User feedback can be provided to help a woman reduce the pain associated with contractions, or to try to reduce the number or frequency of contractions. For example, user feedback may include recommendations for a better body position, for specific food, for specific activities (such as take a hot bath), etc. In a further embodiment, providing user feedback recommendations or suggestions provided to the pregnant woman based on the relationship between contractions and maternal stress level, calculated at 340. For example, if an increased number of contractions and an increased stress level are detected simultaneously, feedback can be provided to the woman to monitor her stress level and to try to relax. The feedback can also include tips to help the user relax, or exercises that the user could do to reduce her stress level. In a further embodiment, providing user feedback recommendations and suggestions can be provided to the pregnant woman based on the relationship between contractions and activity of the mother, calculated to 520. For example, if an increased number of contractions and an increased activity level are detected simultaneously , feedback can be provided to the woman to reduce her activity and stay calm for a few days. The user feedback can take the form of a message displayed in an APP for smartphone, tablet, smart watch, or smart glasses, or an SMS message sent to the mother. Alternatively or additionally, the feedback may also take the form of a message, chart, image, figure, or other multimedia messages sent to the pregnant woman's partner, family, or friends. Alternatively or additionally, the user feedback may take the form of a report with graphs, tables or text, sent to a gynecologist or clinically trained staff for further interpretation.
According to the method 100 for monitoring uterine contractions of FIG. 5A, FIG. 5B, FIG. 6, FIG. 8, FIG. 9A, FIG. 9B, FIG. 9C, FIG. 9D, FIG. 10, FIG. 11, or FIG. 12, the data collected may include at least one of UC statistics, contraction type, mHR, mHRV, maternal stress level, fHR, hHRV or maternal activity. In yet another alternative embodiment, the data collected by using method 100 for monitoring uterine contractions can be used to stratify pregnant women into different patient categories based on their lifestyle and / or physiological profiles. In a further embodiment, the data collected by using method 100 for monitoring uterine contractions can be used to identify potential risk factors for pregnancy complications or negative outcomes. Pregnancy complications can include hypertension, gestational hypertension, gestational diabetes, preeclampsia, etc. Negative pregnancy outcomes can include premature birth, low birth weight, miscarriage, etc.
The method 100 for monitoring uterine contractions can be achieved, according to an embodiment appended herein, by the device 2000 for contraction monitoring illustrated in FIG. 13.
With a view of FIG. 13, the device 2000 for contraction follow-up includes an electrode patch 2100 and a sensor module 2200, conveniently combined to track at least one channel of uterine contraction signals. The electrode patch 2100 and the sensor module 2200 can be in one part or in two different parts. The two separate parts can be supplied with a mechanical and electrical system to attach themselves to each other, such as a clamp system, a magnet. Other embodiments are described in the description.
FIG. 14 illustrates another embodiment of the device 2000 for contraction follow-up. FIG. 13 and FIG. 14, one can easily understand that the electrode patch 2100 or the sensor module 2200 can easily assume many different form factors.
In other words, the device 2000 for contraction follow-up can take many different shapes, sizes, colors, materials and bendability to the body. The device 2000 may or may not take the form of a plaster. For example, the 2000 device can be processed into a piece of clothing. Or the device 2000 can take the form of a belt worn around the lower abdomen. For the latter two examples, the electrode patch 2100 can be an integral part of a garment, or belt, or it can be attached to a garment or belt. FIG. 15 shows an exemplary embodiment of the contraction follow-up device 2000, in which the electrode patch 2100 and the sensor module 2200 can be integrated and enclosed in a unique part that completely forms the device 2000.
Preferably, the contraction tracking device 2000 of FIG. At least three electrodes, one of which is a measurement electrode located at one end of the device, a reference electrode located at the other end of the device, and a bias electrode in the center. This configuration allows the measurement of a channel EXG signal along the horizontal direction. Preferably, the device 2000 of FIG. 4 electrodes, two measurement electrodes located at the two ends, a reference electrode located in the center of the device, and a bias electrode located between a measurement electrode and the reference electrode. Advantageously, a variant of the device 2000 of FIG. 15 (not shown) have electrodes, two measurement electrodes located at the two ends of the device, a reference electrode located at the center of the device 2000, an additional measurement electrode located below the reference electrode, at 90 degrees from the line between the first three electrodes, and a bias electrode located between a measurement electrode and the reference electrode. This configuration allows the measurement of two channels of EXG signals, one along the horizontal direction and one along the vertical direction. In a further exemplary embodiment, the device 2000 can be attached to the body through the use of an adhesive layer. In another embodiment, the adhesive layer can be replaced by the user. In another exemplary embodiment, the device 2000 can be attached to the body by means of a strap or a piece of textile that can keep the device 2000 in contact with the body. FIG. 16 shows another exemplary embodiment of the contraction follow-up device 2000, in which the electrode patch 2100 and the sensor module 2200 can be two different parts of the device. The sensor module 2200 can be attached to the electrode patch before it is used for contraction monitoring. Preferably, the contraction device has succession 2000 of FIG. 16 at least three electrodes, one of which is a measurement electrode located at one end of the device 2000, a reference electrode located at the other end of the device 2000, and a bias electrode in the center. This configuration allows the measurement of an EXG signal along the horizontal direction. Preferably, the device 2000 of FIG. 16 4 electrodes, two measurement electrodes located at the two ends, a reference electrode located in the center of the device 2000, and a bias electrode located between a measurement electrode and the reference electrode. The electrode patch 2100 can be reusable and washable. Alternatively, the electrode patch 2100 may be disposable, meaning that the user may replace the electrode patch 2100 after each use of the contraction tracking device 2000. FIG. 17 shows an exemplary embodiment of the contraction follow-up device 2000, in which the electrode patch 2100 and the sensor module 2200 can be integrated in a textile or clothing accessory. Examples of clothing accessories can be included but are not limited to a shirt, T-shirt, belly belt, a maternity support belt or a belt. Preferably, the contraction tracking device 2000 of FIG. 16 may have at least three electrodes disposed adjacent to each other such that a measurement electrode is located on the right side (respectively left side) of the lower abdomen, and a bias electrode in the center. Preferably, the device of FIG. 15 have a fourth electrode positioned at 90 degrees from the linear placement, in the center of the lower abdomen. This fourth electrode can provide a measurement of the EXG signals in the vertical direction. Preferably, the device 2000 of FIG. 15 have a fifth electrode, positioned on the woman's back, to provide a signal that is free of uterine activity but carries physiological and uptake artifacts, which can be used in processing the EXG signals to produce cleaner and more accurate EHG, obtain mECG and fECG signals. FIG. 18 shows an exemplary embodiment of the contraction follow-up device 2000, in which the electrode patch 2100 and the sensor module 2200 can be integrated into an everyday life accessory that can be positioned on a woman's lower abdomen. For example, the electrode patch 2100 and the sensor module 2200 can be integrated into a pillow or a pillowcase.
As can be seen from FIG. 13, FIG. 14, FIG. 15, FIG. 16, FIG. 17, or FIG. 18, the device 2000 for contraction follow-up is integrated into a small and easy-to-use form factor that does not need to be controlled by clinical personnel. In other words, the device 2000 for contraction follow-up is favorably applied in such a way that a pregnant woman can control it herself. The small size and the extreme minimization can be achieved thanks to low-current electronics system design, which is a combination of low-current circuit design, low-current architecture design and firmware optimization. Low-current system design allows a minimization of the size of the battery and can therefore be a very small size of the overall system. The ease of use can result from a combination of smart electronics and a high level of integration. With smart electronics, the device 2000 can automatically turn on when placed on the body, or the device 2000 can automatically detect contradictions and generate relevant feedback, or the system can automatically detect a specific situation - for example, when a woman is moving - and signal processing adjust accordingly. With a high level of integration, the electrode patch can integrate all cables into the electrode, and provide a very simple way for the user to connect the sensor 2200 to the electrode patch. The connection of the electrode patch 2100 to the sensor 2200 can be done by magnetic interface, by a clamping mechanism, by a sliding mechanism, by a screw mechanism or by any other mechanisms that provide good mechanical and electrical contact between the sensor module 2200. and the electrode patch 2100.
The use of an electrode patch 2100 promotes the reliability of contraction monitoring since it is not possible for the user to misplace the different electrodes relative to each other, since they are always in the same relative position. The use of an electrode patch 2100 enhances the experience and ease of use of contraction follow-up since it is not required to attach multiple electrodes to the lower abdomen, but only requires attaching a single electrode patch.
The 2000 device can be designed in a way that it is clear to the pregnant woman how to carry the device and where to place it. The 2000 device can be designed so that it is very easy to install. Preferably, the pregnant woman should simply take the sensor module 2200, connect it to the electrode patch 2100, and wear it. The electrode patch 2100 comprises at least two electrodes, referred to as the measurement electrode and the reference electrode, which allows the measurement of a channel bipotential (EXH) signal. In an alternative embodiment of the device, the electrode patch 2100 may include a third electrode, which may be used to adjust the signal acquisition electronics to the body voltage, or to apply a common-mode voltage to the body to reduce the noise of the measurement, a measurement principle also known as a right-leg drive. In another alternative embodiment of the device 2000, the electrode patch 2100 may include additional measurement electrodes, which allow the measurement of different channels of EXG signals, leading to multiple channels of uterine contraction signals. The multiple measurement electrodes can be positioned at different locations on the lower abdomen, giving a multi-dimensional measurement of uterine electrical activity. The electrodes may or may not include conductive gel. Conductive gel can be used to promote the quality of contact between the body and the electrodes. The electrode patch 2100 may or may not be adhesive.
In a preferred embodiment, according to the method 100 for contraction follow-up of FIG. 1 and FIG. 2A, the electrode patch 2100 integrates three measurement electrodes, a reference electrode and a bias electrode. With a view of FIG. 2A, the reference electrode 3002 is positioned light below the navel 3001. The three measurement electrodes (3004, 3005, 3006) are positioned on the right, left, and below the reference electrode 3002, respectively. The distance between the reference electrode 3002 and each measurement electrode (3004) , 3005, 3006) is between three and ten centimeters. The bias or right leg drive electrode 3003 can be positioned anywhere on the lower abdomen, but not too close to the other electrodes. Preferably and advantageously, all electrodes can be integrated into an electrode patch 2100. The electrode patch 2100 can significantly improve the reliability, experience and use of the method for contraction tracking of FIG. 1 promote.
The sensor module 2200 may have the electronic wiring required to measure the EXG signals and derive uterine contraction signals according to the method 100 for uterine contraction follow-up of FIG. 1. FIG. 19 shows an exemplary block diagram of an embodiment of the sensor module 2200 of FIG. 13-18. With a view of FIG. 19, the sensor module 2200 includes:
An EXG signal acquisition module 1100,
A signal processing module 1200,
At least one of a memory module 1300 or a data transmission module 1400,
An energy management module 1500, and
A sensor control module 1600.
The EXG signal acquisition module 1100 requires at least one channel EXG signal measured with the electrode patch 2100. In another embodiment (not shown) of the sensor module 2200 of FIG. 19, the EXG signal acquisition module may further comprise: a conditioning module, a gain module, an analog filter module, and an analog-to-digital conversion module. Preferably, the conditioning module conditions the EXG signals for the gain. For example, the conditioning system consists of removing the DC component from the EXG signals, or filtering the EXG signals. In another, more advanced example, the conditioning module includes an analog artifact filter that filters out artifacts such as motion artifacts. Additionally, the gain module amplifies the EXG signals to a level that is compatible with the rest of the sensor module electronics. Even more favorably, the analog filter module further filters the amplified EXG signals, for example to avoid aliases during the analog-to-digital conversion. The analog-to-digital conversion module can convert the analog EXG signals to digital EXG signals. In another alternative embodiment, the EXG signal acquisition module 1100 may further comprise a digital motion artifact filter. The digital motion artifact filter system can further filter the digital EXG signals to remove specific artifacts and promote the quality of the EXG signals.
Preferably, the signal processing module 1200 may process the EXG signals to derive the UC signals according to the method 100 for contraction tracking of FIG. 1. In an alternative embodiment, the signal processing module 1200 is configured to process the EXG signals to derive the UC signals, to 120 and to process the UC statistics, to 140, according to the method 100 for contraction tracking of FIG. 2. In another alternative embodiment, the signal processing module 1200 may process the EXG signals to derive the UC signals, and at least one of the UEMG signals, the mECG signals the mHR, the mHRV, the fECG signals, the fHR or the fHRV. In another alternative embodiment, the signal processing module 1200 may include dedicated processing blocks to remove artifacts from the EXG signals after the EXG signals have been acquired by the EXG signal acquisition module and for any further processing. The signal processing module 1200 can be implemented in a digital signal processor (DSP), in a micro-controller unit (MCU), in a field-programmable gate array (FPGA), in an application-specific integrated circuit (ASIC), in an application specific processor (ASP), etc.
Preferably, the memory module 1300 stores the data corresponding to at least one of the signals generated by the signal processing module 1200. The data can be stored in a volatile or non-volatile manner. For example, the data can be stored on a FLASH memory.
The data transmission module 1400 transmits at least one of the signals generated by the signal processing module 1200 to a user personal device. The user's personal device can be a smartphone, a tablet, a smart watch, smart glasses, a personal computer and / or any multimedia device that is equipped with wireless or optical communication. Wired communication can be achieved by using USB, Ethernet, HDMI, FireWire, Thunderbolt, RS232 or any other wired communication protocol. Wireless communication can be achieved through Bluetooth, Bluetooth low-energy, WiFi, Zigbee, NFC or any other wireless communication protocol.
The energy management module 1500 can supply power to the various modules of the contraction follow-up device 2000. In an alternative embodiment, the energy management module 1500 may comprise energy management circuitry, a battery and on / off circuitry. The energy management wiring can convert the battery voltage to the correct level of input voltage for the different modules of the device. The energy management module 1500 can provide an input voltage that is specific and can be different for each module. The battery can be rechargeable or alkaline, and can be of a different chemistry or form. In the case of a rechargeable battery, the energy management wiring may also include recharging wiring. The on / off wiring can be a switch that allows the user to turn the device on and off. In another alternative embodiment, the on / off wiring may conveniently include electronic wiring to detect when the electrode patch 2100 is connected to the sensor module 2200. The device 2000 may automatically start upon detection of a connection between the sensor module 2200 and the electrode patch 2100, and / or shutdown when the sensor module 2200 is separated from the electrode patch 2100, and thus enhance the user experience. In yet another alternative embodiment, the on / off wiring may conveniently include electronic wiring to detect when the contraction tracking device 2000 is attached to the body. The device 2000 can then automatically start upon confirmation of the contraction follow-up device 2000 to the body, and / or switch off upon the removal of the contraction follow-up device 2000 from the body, thus promoting the user experience.
The sensor module 1600, manages the control of the sensor module 2200, and ensures that the EXG signal acquisition module 1100, the signal processing module 1200, the memory module 1300 and the data transmission module 1400 can work together in an efficient implementation of the sensor module 2200 Preferably, the sensor module can be implemented in a micro-controller unit. FIG. 20 shows an alternative embodiment of the sensor module 2200 of FIG. 19, wherein the sensor module 2200 may further comprise an intertional motion sensor module 1700. The intertial sensor module 1700 may comprise at least a single-axis accelerometer, a two-axis accelerometer, or a three-axis accelerometer. The inertial motion sensor module 1700 can also include a one, two, or three-axis gyroscope, and / or a one, two, or three-axis magnetometer. The inertial motion sensor module 1700 can be used to monitor the general movement of the pregnant woman, or the movements of the lower abdomen caused by fetal movements and spades. Alternatively or additionally, the inertial motion sensor module 1700 can be used to measure the local motion of the sensor module. In a further embodiment, the data originating from the inertial motion sensor module can be used by the signal processing module 1200 to filter artifacts from the EXG signals. FIG. 21 shows an alternative embodiment of the sensor module 2200 of FIG. 19 or FIG. 20, wherein the sensor module 2200 may further comprise a contact impedance measurement module 1800. The contact impedance measurement module 1800 can be used to measure continuously or in between the impedance of the contact of each electrode with the body. Thus, the contact impedance can advantageously be used to provide an estimate of the quality of the contact, or to provide an estimate of the movement artifacts. In a further embodiment, the contact impedance signals may be used by the signal processing module 1200 to filter artifacts from the EXG signals. FIG. 22 shows an alternative embodiment of the sensor module 2200 of FIG. 19, FIG. 20, or FIG. 21, wherein the sensor module 2200 may further comprise a user interface module. The user interface module 1900 can be any of any combination of: Light Emitting Diode (LED), a set of LEDs, the individual LED can be positioned in a shape that represents a specific shape such as a circle, a rectangle, a triangle or any other geometric shape. The LED color and / or the activation pattern can be designed to convey a different message to the user. For example, the LED can be used to communicate system start-up, system shutdown, battery level, the detection of a contraction, the intensity of a contraction, the frequency of a contraction, the duration between two contractions, etc. The LED can be controlled by the sensor control module 1600. FIG. 23 shows an alternative embodiment of the contraction tracking device 2000 of FIG. 13-18, in which the contraction follow-up device 2000 is used in combination with at least one personal device 2001. The user personal device 2001 can be a smartphone, a tablet, a smart-watch, smart-glasses, a personal computer and / or similar which multimedia or digital device. In a further embodiment, the data recorded and transmitted by the contraction tracking device 2000 to user personal device 2001 may be displayed and / or stored on the user personal device 2001. In yet a further embodiment, the data recorded and transmitted by the contraction follow-up device 2000 can be sent further, via the user personal device 2001, to a cloud-based server or database 2002.
In another alternative embodiment of the contraction follow-up device 2000 of FIG. 13-18, the data of the contraction follow-up device 2000 can be combined with the data measured by a health or well-being follow-up device. For example, the data from the contraction follow-up device 2000 can be combined with the data from a scale, an activity tracker, a heart rate chest belt, a wristband heart rate monitor, a basic temperature sensor, or with any other ambulatory, portable follow-up system.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to monitor at least one UC signal and / or UC statistic, and to visualize at least one UC signal and / or statistic on the screen of the user personal device 2001. To accomplish this task, the EXG signal acquisition module 1100 can achieve at least one EXG signal, and the signal processing module 1200 can process and analyze the EXG signals according to the method 100 of FIG. 1, FIG. 2, FIG. 3A, FIG. 3B or FIG. 4. For example, with a view of FIG. 1, the EXG signals can be further processed to derive EHG signals, to 120, the EHG signals can be further processed to derive UC signals, to 130, and UC signals can be analyzed to calculate relevant US statistics, to 140. The data transmission module 1400 can then send at least one UC signal to the user personal device 2001 for visualization.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to classify contractions, wherein the signal processing module 1200 of FIG. 15 can process and analyze the EXG signals according to the method 100 of FIG. 5A or FIG. 5B. With a view of FIG. 5A or FIG. 5B, the EXG signals can be processed to derive UEMG signals, to 210, the UEMG signals can be further processed to derive UEMG markers, to 220, and the UEMG markers can be used to classify contraction types, to 230 In a further embodiment, the information about the contraction classification may be displayed to the user via the user interface module 1900 or on its user personal device 2001. In a further embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to distinguish Braxton Hicks contractions from real birth contractions.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to detect a birth in which the signal processing module 1200 of FIG. 15 can further analyze the UC signals and / or the contraction type to detect delivery, at 240, according to the method 100 of FIG. 6.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to monitor stress level of the mother, wherein the signal processing module 1200 of FIG. 15 can process and analyze the EXG signals according to the method 100 of FIG. 8. With a view of FIG. 8, the EXG signals can be processed to derive mECG signals, at 310, the mECG signals can be further processed to derive mHR and mHRV, at 320, and mHR and mHRV can be further processed to reduce stress level of the mother to derive, at 330. In a further embodiment, the UC signals and / or UC statistics may be associated with the stress of the mother, at 340, and according to the method 100 for contraction monitoring of FIG. 8. The relationship between contractions and strtess level can be visualized in a graph that overlays the UC statistics and the stress level. For example, the number of contractions per hour can be visualized together with the average stress level per hour. In another embodiment, the relationship between contractions and stress level can be summarized in a correlation score that summarizes in a figure to what extent stress is related to contractions for a particular user of the system. Thus, the contraction follow-up device 2000 of FIG. 13-18 can be used to relate UC signals and / or UC statistics to maternal stress level, providing a new understanding of the relationship that may exist between maternal stress and contractions.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to monitor UC signals and at least one of fECG, fHR or fHRV, wherein the signal processing module 1200 of FIG. 15 can process and analyze the EXG signals according to the method of FIG. 9A or FIG. 9B. With a view of FIG. 9A or FIG. 9B, the EXG signals can be further processed to derive fECG signals, at 410, and the fECG signals can be further processed to derive fHR and fHRV, at 420. In a further embodiment, the contraction tracking device 2000 can be FIG. 13 can be used to monitor the position of the fetus, according to the method 100 of FIG. 9C. In a further embodiment, the contraction tracking device of FIG. 13-18 are used to monitor the movement of the fetus, according to the method 100 of FIG. 9D.
In yet another alternative embodiment, the contraction tracking device 2000 of FIGS. 13-18 can be used to visualize the UC signals and the fHR together. The signal processing module 1200 can simultaneously derive the UC signals and the fHR, and can relate UC signals to fHR according to the method 100 of FIG. 10. The fHR and the UC signals can be sent to the user personal device 2001 where they can be displayed on top of each other, such as the way signals are displayed on a cardiotocogram in a hospital environment. Stated another way, the contraction monitoring device 2000 provides FIG. 18 information similar to a cardiotocogram but with a much greater user comfort since the contraction follow-up device can be made much smaller and much more comfortable than the use of traditional belted cardiotocogram probes.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to monitor activity of the mother, according to the method 100 for contraction monitoring of FIG. 11. In one embodiment, the mother's activity can be measured with the inertial sensor module 1800 of FIG. 21. In another embodiment, the activity of the mother can be measured by using an activity sensor integrated in the user personal device 2001, such as a smartphone. In yet another embodiment, the mother's activity can be measured by using a separate activity tracker that can be connected to the user personal device 2001. In yet another embodiment, the UC signals and / or the UC statistics can be associated to the activity of the mother, by associating contractions with activity of the mother, to 520, according to the method 100 for contraction follow-up of FIG. 11. The relationship between contractions and activity of the mother can be visualized using a graph that superimposes the UC statistics and the activity of the mother. For example, the number of contractions per hour can be visualized together with the accumulated and / or average activity per hour. In another embodiment, the relationship between contractions and activity of the mother can be summarized in a relationship that summarizes in a figure to what extent activity of the mother is related to contractions for a particular user of the system. So the contraction follow-up device 2000 of FIG. 13-18 can be used to relate UC signals and / or statistics to maternal activity, providing a new understanding of the relationship that may exist between maternal activity and contractions.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 are used to monitor fetal activity, according to the method 100 for contraction follow-up of FIG. 12. In one embodiment, the fetal activity can be derived from the fECG, by using fECG processing to divert fetal movement, at 430, according to the method 100 for contraction follow-up of FIG. 9D. In another embodiment, the fetal activity can be measured with the inertial sensor module 1800 of FIG. 21. In yet another embodiment, the UC signals and / or UC statistics may be associated with fetal activity, by associating contractions with fetal activity, to 620, according to the method 100 for contraction follow-up of FIG. 12. The relationship between contractions and fetal activity can be visualized in a graph that superimposes UC statistics and fetal activity. For example, the number of contractions per hour can be visualized together with the accumulated and / or average fetal activity per hour. In another embodiment, the relationship between contractions and fetal activity can be summarized in a correlation score that summarizes in a figure to what extent fetal activity is related to contractions. Thus, the contraction follow-up device 2000 of FIG. 13-18 can be used to link UC signals and / or statistics to fetal activity, providing new insights into the relationship that may exist between fetal activity and contractions.
In yet another alternative embodiment, the contraction tracking device 2000 of FIG. 13-18 provide the user with feedback, according to the method 100 for contraction follow-up of FIG. 13-18. Feedback may include further information about the data recorded with the contraction follow-up device. Alternatively or additionally, feedback may include recommendations for lifestyle or behavioral adjustments based on the data measured by the contraction follow-up device. Feedback can be provided via the user interface module 1900 of FIG. 22, or via the user personal device 2001 of FIG. 19 and / or via any other means of communication from the user. Feedback can take the form of a sound, a certain LED pattern, a text message, a voice message or any other multimedia communication.
The released embodiments are susceptible to various modifications and alternative forms, and specific examples thereof are shown by way of example in drawings and are described herein in detail. It is to be understood, however, that the released embodiments should not be limited to the specific released forms or methods, but rather, the released embodiments should include all modifications, equivalents, and alternatives.

权利要求:
Claims (16)
[1]
Conclusions
A system for monitoring contractions in a pregnancy comprising: - an electrode patch comprising at least two electrodes, a measurement electrode and a reference electrode, and - a sensor module configured to be connected to the electrode patch, the sensor module comprising a signal acquisition module, a signal processing module, an energy management module, a sensor control module, an inertial motion sensor and at least one of a memory module or a data transmission module; wherein the signal acquisition module is configured to measure bio-potential signals by at least two electrodes provided on the lower abdomen of a pregnant woman and to receive intertional motion data via the inertial motion sensor, and wherein the signal processing module is configured to perform a method which comprises the following steps: a) processing the intertional motion data to measure a motion artifact signal, b) processing the bio-potential signals to extract electrohysterography (EHG) signals, maternal electrocardiogram (ECG) signals and fetal electrocardiogram (ECG) signals c) applying motion artifact filtering to the extracted signals based on the motion artifact signal; d) processing the extracted and filtered signals to derive uterine contraction signals, and e) analyzing the uterine contraction signals to calculate uterine contraction statistics.
[2]
The system of claim 1, wherein the method performed by the signal processing module further comprises: isolating from the EHG signals the portion of the EHG signals relevant to the electrical activity of the uterus, and separating them from the portion of the EHG that is related to other physiological phenomena, noise, artifacts and any other additions.
[3]
The system according to claim 1 or 2, wherein the method performed by the signal processing module further comprises: processing of bio-potential signals to derive uterine electromyogram (UEMG) signals, - processing of UEMG signals to derive UEMG markers, and - classifying the bio-potential signals in contraction types based on the UEMG markers.
[4]
The system of claim 3, wherein the UEMG markers comprise: - statistical properties, such as at least one of an average, percent, standard difference, kurtosis or any other statistical method, energy spectrum properties, such as at least one of total energy in the bandwidth, peak current, average current, current and certain frequency bands, entropy properties, and spatial propagation properties, such as at least one of a laplacian, gradient, and higher order propagation properties, to the speed at which uterine contraction cells fire, the pattern in which they indicate the spatial distribution of firing.
[5]
A system according to any of the preceding claims, further comprising an activity sensor, wherein the method performed by the signal processing module further comprises measuring activity of the mother using the activity sensor.
[6]
The system of claim 5, wherein the method performed by the signal processing module further comprises: correlating the activity of the mother with the uterus contraction signals and / or the uterus contraction statistics.
[7]
A system according to any of the preceding claims, wherein the method performed by the signal processing module further comprises: measuring the activity of the fetus by means of the inertial motion sensor.
[8]
A system according to any of the preceding claims, wherein the motion artifact signal is a contact impedance measured with the aid of the at least two electrodes.
[9]
The system of any one of the preceding claims, wherein the inertial motion sensor module further comprises: at least one of a one-axis accelerometer, a two-axis accelerometer or a three-axis accelerometer.
[10]
The system of any one of the preceding claims, wherein the inertial motion sensor module further comprises: at least one of a one-axis gyroscope, a two-axis gyroscope or a three-axis gyroscope.
[11]
The system of any one of the preceding claims, wherein the inertial motion sensor module further comprises: at least one of a one-axis magnetometer, a two-axis magnetometer, or a three-axis magnetometer.
[12]
The system of any one of claims 7 to 11, wherein the method performed by the signal processing module further comprises: correlating the uterus contraction signals and / or the uterus contraction statistics with the activity of the fetus.
[13]
A system according to any of the preceding claims, provided with three measurement electrodes, a reference electrode and a bias electrode, wherein the first and the second measurement electrode and the reference electrode are positioned substantially in one line, and wherein the third measurement electrode is below the reference electrode is positioned substantially perpendicular to the line.
[14]
The sensor module for use in the system of claim 1, configured to be connectable to the electrode patch, comprising the signal acquisition module, the signal processing module, the energy management module, the sensor control module, the inertial motion sensor, and the at least one of a memory module or a data transmission module.
[15]
The contraction follow-up apparatus for use in the system of claim 1, comprising an electrode patch and a sensor module adapted to communicate with a personal user device, which personal user device is configured to perform portions of the signal processing module and the signal acquisition module functions performed.
[16]
The contraction follow-up apparatus according to claim 15, wherein the functions performed by the personal user device comprise: the use of context information from the personal user device to improve the accuracy and reliability of an estimate of the user's uterine contraction activity , an estimate of maternal activity, an estimate of maternal stress, an estimate of an HR / HRV of the fetus, an estimate of movement of the fetus, an estimate of a position of the fetus; providing feedback to the user in accordance with the measured parameters and estimated information to promote a healthy lifestyle and to reduce pregnancy-related risks.

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同族专利:

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公开号 | 公开日

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BE1024055A1|2017-11-08|

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法律状态:
2018-02-08| FG| Patent granted|Effective date: 20171109 |

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2019-11-20| MM| Lapsed because of non-payment of the annual fee|Effective date: 20190228 |

优先权:

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US201462072348P| true| 2014-10-29|2014-10-29|

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US62/072,348|2014-10-29|

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