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    • 专利摘要:
    Methods and apparatus provide Cheyne-Stokes respi ration ("CSR") detection based on a blood gas measurements such as oximetry. In some embodiments, a duration, such as a mean duration of contiguous periods of changing saturation or re saturation occurring in an epoch taken from a processed oximetry signal, is determined. An occurrence of CSR may be detected from a comparison of the duration and a threshold derived to differentiate saturation changes d.ue to CSR respiration and saturation changes due to obstructive sleep apnea. The threshold may be a discriminant function derived as a classifier by an automated training method. The discriminant function may be further implemented to characterize the epoch for CSR based on a frequency analysis of the oximetry data. Distance from the discriminant function may be utilized to generate probability values for the CSR detection. -34 -
    公开号:AU2013221962A1
    申请号:U2013221962
    申请日:2013-08-29
    公开日:2013-09-19
    发明作者:Jeffrey Peter Armitstead；Chun Yui Lau
    申请人:Resmed Pty Ltd；
    IPC主号:A61B5-087
    专利说明:
    TISCRIMINATION OF CHEYNE-STOKES BREATHING PATTERNS BY USE OF OXIMETRY SIGNALS CROSS-REFERENCE TO RELATED APPLICAT IONS [0001] This application claims the benefit of the filing date of 'U.S. Provisionl Patent Application No. 61/170, 734, filed April 20, 2009, the disclosure of which is hereby incorporated herein by reference FIELD OF THE TECHNOLOGY [0002] This technology relates to the discrimination of breathing abnormal_ties by applying quantitative measures to a physiological signal for use as a clinical decision-support tool. In particular it relates to the discrimination of Cheyne-Stokes Respiration ("CSRP") by the analysis of oximetry signals, which may optionally be in conijunct ion with flow signals. The technology may also relate to the training or a classic ier able to provide probability values for CSR discrimination. The technology may also relate to techniques for improving the readout of oximetry signals by removing artifacts recognizable in the context of CSR. BACKGROUND OF THE TECHNOLOGY [0003] The diagnosis of CSR usually involves conducting a sleep study and analyze n g the resu lting polysomnography ("PSG") data. In a full diagnostic PSG study, a range of biological parameters are monitored that typical ly include a nasal flow signal, measure of respiratorV effort, pulse oximetry, sleeping posi tin and may include: electroencephai oqrap y ("EEG") , e le crocardiography ("ECG") , electromyographv ("EMG") and elec tro- ocu ography ( EOG") . Breathing cnaracteristics are aiso identified from visual features, thus allowing a clinician to assess respiratory function during s leep and evaluate any presence of CSR. [0004] During a period of Che-yne-Stokes breathing or CSR, patterns of waxing and waning tidal volume can be seen in a nasal flow signal, which is a direct measure of pulmonary functions. This unstable behavior of breathing often extends is presence into other cardic-respiratory parameters such as - 1 blood oxygen saturation levels where cyclical changes can be seen. [0005] While the examination by a clinician is the most comprehensive method, it is a costly process and depends heavily upon clinical experience and understanding. For effective and efficient screening of patients, a classifier algorithm has been developed by the assignee of this invention that automates the scoring process by calculating the probability of a CSR occurring based on a nasal flow signal. The algorithm is disclosed in US patent application SN 11/576,210 (U.S. Patent App. Pub. No. 20080177195) filed March 28, 2007, and published as W02006066337Al June 29, 2C006. The existing alcorithm is a flow-based classifier where a probability of CSR is calculated given a sequence of- discrete flow values. A series of pre-processing steps are performed such as linearization of flow values, filtering and the extraction of respiratory events. [0006] The concept of a classier is common to many fields where it is desirable to assign an object or an uncl state of an ob-ject to one of a number of classes. This concept is used, for example, in the fields of voice recognition (where sound bytes are classified as different words or syllables) , radar detection (where visual signals are classified as enemy/friendly targets) and medical diagnosis (where test results are used to classiry a patient's disease state) Th e design of a classifier falls under the field of Pattern Recognition and a classifier can be of the supervised type (the classifier is built from training data which has been pre-classed by a supervisor or "expert") or unsupervised type (where the natural ordering or clustering of the data determines the different classes) .Time signal classification usually relies on representing the signal at particular time points with "features". Features are simpiv numbers that distil the essence of the signal at a point in time, a form of compression. A set (or Vec tor) of features is called a " pattern" . A classifier takes a pattern and manipulates it mathematically with a suitable algorithm to produce a probabilitv value for each of a number of classes. The pattern is assigned to the class with the highest probability. [0007] Home pulse oximetry has been proposed as an alternative tool for identification of CSR, but relies on visual inspection of the oximetry signal by a trained observer (Staniforth et al., 1998, 1-eart, 79:394-99) . [0008] A study of 104 sub-jects with Congestive Heart Failure ("CHF") by Staniforth et al. (1998, Heart, 79, 394-399.) has examined the dc-saturation index recorded in nocturnal oximetry compared to normal controls. The model yielded a specificity of 81% and a sensi tivity of 87% for detecting CSP CSA. However, the overall accuracy of the model was not prov-ded. Those authors made no attempt to determine if pulse oximletry could be used to distinguish between CSRP-CSA and Obs'trucive Sleep Apnea ('OSA'). U.S. Patent No. 5,575,285 Takanashi e al, describes measuring oxygen saturation in blood from scattered and transmitted light and performing Fo ur transformation to obtain a power spectrum over a requency range of 500 -z to 20 kHz. However, that described method does not allow for distinction between patients with CSR and OSA. [0009] U.S. Patent No. 6,839,581 to Grant et al, PCT Application No. W O 01/076459 and U.S. Published Patent Application No. 2002/0002327 are entitled "Method for Detecting Cheyne-Stokes Respiration in Patients with Congestive Heart Failure." The :jointly propose a diagnostic method for CSR including performing overnight oximetry recordings and performing spectral analysis on the oximetry recordings. A classification tree or neural network based on parameters der ived ffrom a power spectral analysis determines the presence or absence of CSR. [0010] U.S. Patent. No , 760,608 to Lynn is entitled "Oximetry System for Detecting Ventilation Instability., This patent. describes a pulse oximetry system used to generate a time series of oxygen saturation values. The occurrence of certain -3patterns along the time series is used to indicate ventilation instabilitV. [0011] U.S. Patent No. 7, 081 , 095 to Lynn et al is entitled. Centralized Hosptal Monitoring Svstem for Automatically Detecting Upper Airwayr Instability n and for Preventing and Aborting Adverse Drug Reaction" It proposes an automatic system of diagnosis of OSA in a computerized environment of a centralized hospital critical care system. [0012] U.S. Patent No. 7,309,31 to Grant et al is entitled MethoLd for Predicting Apnea-yponea Index From Overnight Pulse Oxiimetry Readings." This patent proposes a tool for predicting an Apopnea Hypopnea Index ("AHI") for use in the diagnosis of OSA by recording pulse oximetry readings, and obtaining a delta index, oxygen saturation times and oximetry de -saturat ion events. A mulitivariate non-parametric analyst is and bootstrap aggregat ion is performed. [0013] U.S. Patent No. 7,398,115 to Lynn is entitled "Pulse OximeLry Relational Alarm System for Early Recognition of Instability and Catastrophic Occurrences. The system described in this patent has an alarm triggered by the early recognition of likely catastrophic occurrences by detecting decreases in 02 saturation coupled with either: a) decrease in pulse rate; or b) increase in respiration rate. The system of this patent is aimed at treating and detecting OSA. [0014] None of these prior art systems are capable of reliably interpreting oximetric data to reliably discriminate OSAs from CSR and to develop a probabilistic value for such attempts at apnea discrimination. SUMMARY OF THE TECHINOLOCY [0015] The present technology enhances the discrimination or CSR based on oximetry. The technology may be applied to enhance the detection performance of a flow-based classifier technology system. Thus, it may enable the screening of CSR to become more accessible. For example, it may be imply cemented as an additional feature to the detection system described in U.S. Patent Application SN 11/576, 210 filed. March 28, 2007, -4 and published as WO 06066337A1 on June 29, 2006. Optionally, the technology may also serve independently or as a sIand alone alternative when a flow signal or data therefrom is unavailable or of unfavorable quality. [0016] The present technology may replace the current screening process wi-th one that is generally more comfortable and easier to use for the patient, easier to administer for the physician and/or less costly to conduct the analysis. [0017] While the present technology may be explained in terms of a sequential process or algorithm, it may be understood that the process or algorithm can be carried out using a non linear, non-sequential, or non-staged process, or the order of the process may be ranged. Whi le this embodiment of the technology describes an entire process, aspects of the technology may relate to only a subset of that process. [0018] A signal representative of respiration, such as an oximetry signal, may be recorded from a patient using a logging device which includes a data-acquisition system and a memory. The respiratory signal may be processed either on board by the recording device or off--line using a computer. [0019] The signal may b e initially pre-processed. For example, the signal car be fi _lered to remove unwanted noise and, where appropriate, e baseline is zeroed. The signal may also be made linear depending on the transducer used to detect the respiration. In part. i cular the technology may include a process for removing artifacts peculiar to oximetric measurements and for developing an oximetry signal quality indicator (QI) that may be used to determine a confidence ev in the discrimination prediction. [0020] In another stage the signal 1s divided into n epochs of equal length. The epoch length can be as long as the entire record or as short as is practicable to enable detection of respiratory patterns. In one example embodiment, the epoch length is 30 minutes. [0021] A CSR-detection algorithm of the present technology alternativelv or in conjunction with oximetry may use h -5nasal fFlow signal from a device sch as MAP's microMesam@ together with pattern recognition techniques to assign a probability of CS breathing to each 30 minute epoch of flow r-ecor-ded. [0022] The technology may provide a method for the calculation of an Event Feature. The method may also include the calculation of a Spectral Feature determined by, for example, Fourier analysis or by the use of Wavelet Transforms. [0023] Another characteristic of CSR, namely saturation delay, may be used to provide a method for calculating the amount of delay of de-saturation and re-saturation dielyed but n synchrony with breathing as a further indicator of CSR. [0024] The technology also may invo-lve a metho--d for training a processor implemented classifier to discriminate CSR and for produoci n a probabiity value for ea epocn segment of oxi metric data for indicating the presence of CSR. [0025] In some embodiments of the technology, a computer implemented method detects an occurrence of Cheyne-Stokes respiration with one or more programmed processors. The method of the processor may include accessing blood gas data representing a measured blood gas signal. It may also include d etermining a duration of one or more contiguous periods of changing saturation of a blood gas from the blood gas data. It may further include detect 1ng the occurrence of Cheyne Stokes respiration from a comparison of the determined duration and a threshold derived to differentiate saturation changes doe to Cheyne-Stokes respirat ion and saturation chances due to obstructive sleep apnea. In some embodiments, the one or more contiguous periods of changing saturation may be re-saturation periods and the measured blood gas signal may be an oximetry signal. In still f further embodiments, he determined duration may be a mean period length and the detecting may indicate an occurrence when the mean period length exceeds the threshold. In some embodiments, the threshold comprises a discriminate function. The detecting the occurrence may optionally involve determining a distance -6 rom the threshold and comparing the distance to a further threshold. The method may also optionallv involve determining a presence of a peak in a predetermined frequency range for desaturation and resaturation cycles of the blood cas data and comparing the determined presence to the dciscriminant function. [0026] Embodiments of the technology may also involve an apparatus to detect an occurrence of Cheyne-Stokes breathing. The apparatus may include a memory for blood gas data representing a measured blood gas signal. The apparatus may also include a processor coupled with the memory. The processor may be configured (a) t-o det-ermine a durat-ion of one or more contiguous perods of changing saturation of a blood gas from the blood gas data and (b) to detect an occurrence of Cheyne-Stokes respiration from a comparison of the determined duration and a threshold derived to different ate saturation changes due to Cheyne-Stokes respiration and saturation changes due to obstructive sleep apnea. In some embodiments of this apparatus, the one or more contiguous periods of changing saturaion may be re-saturation periods when the measured. blood gas signal is an oximetry signal, which may be measured by an optional oximeter. in some embodiments, this determined duration may be a mean period length and the detecting may indicate an occurrence when the mean Period length exceeds the threshold, which may optionally be a discriminant function. Tn processor apparatus may also be configured to detect the occurrence by further determining a di stance from the discriminant function and comparing the distance to a further threshold. In still further embodiments, the processor can be configured to determine a presence of a peak in a predetermined frequency range for de saturation and re-saturation cycles of the blood gas data and then compare the determined presence to the discriminate funct ion.
    [0027] Other features of the technology will be apparent from consideration of the information contained in the following detailed description, abstract and claims. BRIEF DESCRIPTION OF THE DRAWINGS [0028] The present techloiogy is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like reference numerals refer to similar elements including [0029] Fig. 1 is an example graph of the amplitude and first difference of an oximetry signal in a patient over a duration of one half hour (1800 seconds; [0030] Fig. 2 shows the mean saturation duration in CSR as a function of time measure in seconds; [0031] Fig. 3 shows the mean saturation duration in OSA as a function of time measured in seconds; [0032] Fig. 4 shows the spectral feature of CSR, where the spectral feature is the difference between the maximum and mean value of the Fourier Transform of the sa uration; [0033] Fig. 5 shows the spectral feature of OSA, where the spectral feature is the difference between the maximum and mean value of the Fourier Transform of the saturation; [0034] Fig. 6 shows the oxygen saturation of representative CSP epochs; [0035] Fig. 7 shows the global wavelet spectrum of CSR as a function f the Fourier-equivalen frequency; [0036] Fig. R shows the computed delay for oxygen saturation, yen i aton and delayed ventilation as a function of time in seconds; [0037] Fig. 9 depicts a decision boundary and its relationship to the distribution of the training set of data; [0038] Figs. 10 and 11 depicts the decision boundary and its relationship to the distribution of the validation set of d a. t a. [0039] Fig. 12 is an example flow chart for process steps involved in modifying the distribution of data or classifying oximetry eoochs for CSR; -8- [0040] Fig. 13 shows schema t call y the use of the classifier of the present technology to screen patients for evidence of CSR as a computer--aided diagnostic tool.; [0041] Fig. 14 shows receiver operating characteristics on a patient-by-patient basis; [0042] FIG. 15 is a further illustration of components of a CSP detection and/or training system of some embodiments of the present technology. DETAILED DESCRIP
    T
    ION [0043] Embodiments of the present technology may include: a system, device, classifier, and/or methods. Example embodiments are here n described with reference to the accompanying drawings and more specifically Figs. 1-13 and 15. [0044] CSR is a form of periodic breathing believed to be due to instability in the central nervous system control of ventilation. Breathing in a CSR sufferer is characterized by a "waxing and waning" tidal volume as respiration alternates between repetitive episodes of apnea/hypopnea and hype rpnea. Recordings of nasal flow signals in a compressed time scale reveal a pattern that is similar to an Amplitude- Modulated ('AM') waveform. [0045] During a period of Chevne-Stokes breathing or CSP, --he pattern of- waxing and waning tidal volume that can be seen in a nasal flow signal as a direct- measure of pulmonary function also is present as cyc ical changes in other cardio respiratory parameters such as blood oxygen saturation levels. For example, during sustained apneic periods, blood oxygen saturation may fall due to the dynamics of he cardio respiratory system. Measurements of oxygen saturation using pulse oxi"met-ry exhib ibt periodic de-saturation and re satutration that mimics the rise and fall of ventilation caused by C'R. [0046] The cycli cal pattern of the blood oxygen saturation levels in CSR differs to that of a serially occurring sequence of Obstructive Sleep Apnea (OSA) events. The patho physiologic mechanism behind the Cheyne-S tokes breathing -9Spattern is associated with the level of arterial partial pressures of carbon dioxide (PaCO 2 ) The presence of allow PaCC) 2 may suppress pat ient's central drive to breathing in response to hypocapnia, which typically initiates shallow breathing and subsequently partial or complete withdrawal of breathing if driven below the apneic threshold, resulting in Central Sleep Apnea (CSA) . Following an apneic period, a subsequent rise in PaCO 2 will develop, which may induce a nyper-ventilatory response. Consequently, a decline in PaCO2 may begin where the cycle would. normally repeat. [0047] This oscillating response to venti lation may result in a waxing and waning tidal volume and a gradually swinging blood oxygen saturation levels. The rising an falling oxygen saturation levels are delayed but may usually be in synchrony with hyperventilation or hypoventilation. The underlying oscillation in the central respiratory drive in association with the cardiac and pulmonary interactions give -rise to an oscillation in oximetric recording that are u-niquely regular during CSR. The spectral feature is intended to capture this pattern of regul arity in the oximetry signal as a marker of the CSR. [0048] Evidence suggests that a compromised cardiac function is a risk factor to conLibuting to CSA. In the stable Congestive Heart Failure (CHF) popullat ion, prevalence rates of CSA ranging from 30% to 50% has been reported (Javaheri et al., Circulation. 1C9;97:215-2159; Sin et al. , Am J Respir Crit Care Med 1999; 6: 1 1 1 06.) . It has also been su pported that a high apneic threshold of PaCC2 predisposes a deveopment of CSA and CSR. [0049] A period of pure Cheyne-Stokes breathing is commonly presented in a PSG study as a serially occurring sequence of CSA events. The development of CSA constituting pure Cheyne Stokes breathing is non-hype rcapnic in origin with typical cycle lengths of 60 seconds (Eckert et al., Chest, 2007; 1_:595-607). It is to be differentiated from other forms such as idiopathic CSA or narcotic-- induced CSA ariLsng from - 10 the application ofl chronic pain medications. These forms or CSA typically have a muon shorter cycle length. The selection of oximetric recordings used for the training of the classifier excludes such data as would be assessed and screened by the clinical expert during the pre-scoring process. This ensures only specific forms of CS A of it erest, are used for the training of the classifier. CSR versus OSA: [0050] Cheyne-Stokes Respiration (CSR) is a form of pe riodic breathing that is typically observed through direct measurement of pu imonary funons such as a. nasal flow recording or airway Ilow recording. Due to the coupling between the cardiac and pulmonar system, CSR may also be identified as alternating periods of desaturation and re saturation through an oximetry signal. Thus oximetry signals may provide a source of information available for the analyst is of Cheyne-Stokes breathing. Benefits of this approach may include the use of oximeters for non-invasively measuring blood oxygen saturation levels, which is an important determinant of a sub-jct's health condition. While oximetry recordings may provide evidence of the occurrence of CSR, or other breathring abnormalities which may also be reflected in an oximetryr signal such as conditions of Cbstructive Sleep Apnea (OSA) . This is preferably taken into account during the training of the classifier to discriminate CSR from OSA. [0051] OSA may be generally initiated by the collapse of the upper airway. During an CSA event, the centraI crive to breathing is not withdrawn as can be seen from the continuing re spiratory effort during a PSG study. Initial breaths following an OSA event is vy ically deep in ef fort with larce tidal volume, which is often assoc ia ted with a rapid rise in oxygen saturation level . In a serially occurr ing sequence of OSA events, rapidly re-saturating oxygen saturarion levels is thus believed to be indicative of an occurrence or OSA. - 11 - [0052] The occurrence of an OSA event is closely related to the mechanical state and anatomy of the upper airway. OSA is driven by the collapseof the harynx, which may happen in- a recurring manner but unlike CSR., it is not- a form of periodic breathing. The variability in the Iengt_-h of: time from the onset of a preceding OSA event to the onset of its successive OSA event tends to be shorter than the ycl e legths of CSR. Oximetry from an OSA recording may find a more episodic pattern of desaturation and re-saturation, lacking the typical regularity found in the cycle l lengths of a pure CSR ox ietry recordig. [0053] However, oximetry signals are contraindicated for use in diagnosing CSR by being pr'on'e to u-desirable artifacts caused by body motion or limb movements. In adult recordings, oxi.meters are commonly placed at the fingertip or ear lobe. The quality of the oximetry signal is highly sensitive to any displacement of the optical sensor in an oxrmeter. Motion artifacts are typically characterized by periods in which abrupt de-saturation and sharp re-saturation occur. It is not uncommon to find that saturation levels are at zero percent within an artifactual period of oximetry recording. There may be a loss of information during this perid, which may be unavoidable. This issue may be overcome by modifying t-he use of an oximetry signal to incorporate a detection scheme that takes into account the abruptness of de-saturatin and re satur nation. [0054] Fig. I depicts an example of an oximetry signal 101,2 and the derivative thereof or a derived oximetry signal 104 from a recording. The signal was recorded during CSR over the duration of a half hour (1800 seconds) . Clear instances of artifacts are shown as the plunge to zero saturation and the sudden recovery. In a system or device of the present technology, data from the signals may be processed according to one or more of the following methodologies. I identifying Artifacts - 12.- [0055] From the derived o x mer- ry (Sp02) signal 104 the beginning of an artifactual period where the signal goes rromi a negative value of less than -10% to a positive value of greater than 10% may be identified. The derived. oximetry signal provides an indication of the beginning and end of an artifactual period, which is marked by an initial sharp negative spike followed by an abrupt positive spike. Artifacts may be removed by linearly interpolating across the region of artifacts. Oximetrv signal quality indicator (QI) [0056] Whereas oximetry measurements have been employed for the detection of OSA, those detection methods are not transf erable to the problem of detecting CO. Te presence of CSR indicates central instability in ventilator control. In pure Cheyne-Stokes breathing flow i s often. associated with central. apneas and hypopneas. In contrast. to obstructive apneas, the resumption of breathing in CSR is usually very gentle, which leads to a slower rare of re-saturation. The present technology takes io account this difference between OSA and CSR, by making use of the mean re-saturation period and th e fact that our statistical analysis shows that only CSR demonstrates re-saturation longer than 10 seconds. [0057] A a quality indicator may be defined for a derived oximetry (SpO 2 ) signal 104 by finding the number T of samples thereof where SpO2 drops below a predetermined percentage threshold such as 10%. The quality indicator (Qi) may be defined as the ratio of T/N where N is the total number of samples considered. However, if this ratio is less than a. threshold of-, for example, about 0.75, the qualit-y indicator may be set to zero. It is also possible to define the equality indicator as a function of the ratio T/N. Calculation of an Event Feature [0058] Once the artifact's have been identified they may be removed from the data. A signal of the remaining data may also be low-pass filtered to derive a filtered signal. The signal can be filtered first ro remove unwanted and -13uninteresting high-frequencv content. For example, the filter used may be a digital Finite impulse Response ("FIR") filter designed using the F ourIer method with a rec angular window. In some embodiments, the filter may have a pas s-band from 0 to z.1 Hz, a transition band from 0.1 to 0.125 Hz and a stop band above 0.125 Hz. The number of terms in the fiter varies with sampling frequency. The signal may be filtered b 1 convolving the tine series point-wise with a filter vector. [0059] Next contiguous periods of re-saturation may be detected. The length of the period may be stored as components of a vector. The event feature may then be calculated as the mean of the component's of the vector. The event feature can be associated with a quality indicator value. Thus, it may be output with a CSR determination based on the particular event feature to provide information to a clinician as to the quality of the CSR detection. [0060] One alternative to extract an event from an oximetry signal may be to derive two filtered signals and then perform a comparison of their varying amplitudes to frame a desaturation event or resaturatin e Ivent. The filter for the first of these derived signals shall have a very low cut-off frequency to represent the long-term saturation signal (SLong) . The filter for the second of the derived signals may have a relatively higher cut-off frequency to represent the short---term saturation signal ( Sihort) . When Sihort falls below a threshold as a percentage of SLong, this may be taken as a trigger for record ng the start o -the desaturation event. When SShort subsequently rises above a threshold above STong, this may be taken as a trigger to record as the end of the desaturation event . A similar process maybe applied to capture a resaturation event. Calculation of a Spectral Feature (SF) [0061] The periodic aternation between apnea/hypopnea and hvperpnea often leads to desaturation and resaturation that are delayed but in synchrony with breathing. The observed oscillation in SpO2 depends on multiple factors, one of which - 14 is the duration of an apnea. Longer apneas are associated with greater desaturation. Figs. 2 & 3 show the diLstribution of mean saturation duration in CSR (Fig. 2) compared to those of OSA (Fig. 3) as a function of time measured in seconds. Observation of various CSR oximetry patterns finds a higher regul arity, in contrast to the episodic nature of oximetry patterns during coritJnuous periods of obstructive apneas. Using a Fourier transform, a spectral feature may measure the presence of a peak in the region near 0. 083Hz to 0.03Hz. [0062] The tendency to de-saturate and re-saturate over longer cvcle times may be taken as a marker o' a CSR abnormalIty. This may be detect-ed or recognized using Fourier-transform techniques to determine individual frequency components and harmonics. Rapid resaturation Ouring post-apneic termination of an OSA event with deep arousal be. aths gives a more episodic style of desaturaton and0 resaturation patterns. This distinguishes the frequency cha ra ct er stics from the more regu a-rly dce-saturation and re-saturation patterns of CSR. [0063] in some embodiments, some or all the following example steps may be imp lamented to determine a Spectral Feature using a Fourier-Transform analysis: . Remove artifacts 2. Divide th.e entie oximetry signal into discrete 30 minutes, 50% over Lapp ng epochs 3. Subtract the signal from 100% 4. Subtract esu t ing signal from an initial value and store tohis valu Low-pass filter the resulting signal Add the initial value stored back to the filtered signal Subtract the resulting signal from 100% 8. De-trend the signal by the mean value 9. Normalize the resuling signal using the Eucidiean norm 10. Calculate the spectrogram with five half-overlapping epochs - 15 - 11. Take the real and absolute magnitude of t s p ect r ogram Extract the 0.083-.03H region and form a new vect or 13. The Spectral Feature (SF) i s calculated as the difference between the maximum and the mean value [0064] Figs. 4 & 5 respectively depict the distribution of spectral feature for CSR and OSA asthe difference between the maximum and mean value of the Fourier-Transform as -juSt described. Use of: Wave let Transforms [0065] Continuous wavelet transform may also be applied to give time-frequency information over the duration of the signal. Fig. 6 shows the oxygen saturation with CSR occurring in a representative epoch Elin such CSR epochs, the wavelet transformed data often results in a ride that can be found or detected in the 2-dimensional data. The wavelet spectrum can be translated from the scale domain (dimensionless) into F ourier-equivalent frequency (Hz) depend ng on the type of wavelet transform used. Fig. 7 shows the global wavelet spectrum as a function of the Fourier-equivalent frequency using the Morlet wavelet as the wavelet function. Epochs with strong presence of CSR often find a spectral peak around the 0.02Hz Fourier--equivalent region . This corresponds well with the Fourier-based spectral peak, as seen in Fic. 7. Thus, in some embodiments of the technology, the peak of the global wave et spectrum may also re used as a spectral feature for the analysis of CSR in oximetrv signal. Delay of Saturation [0066] The periodic alternation between apnea/hypopnea and hyperpnea often leads to desaturation and resaturation that are delayed but in synchrony with breathing. This Delay of the Saturation ("DoS") level response is a result of the complex cardio-respiratory dynamics. Some or all of the steps - 16 of the following method may be used in some embodiments to extract the delay algorithmically. 1. S Ta are the flow signal 2. Low-pass filter the squared flow signal 3 . Suar e- root the result inn signal 4. Down-samole the signal to the equivalent frequency of the nimetry signal to give the ventilation signal 5. Normalize the ventilation signal by the absolute maximum value 6. Subtract the oximetry signal from 100% . I Normalized by the absolute maximum value 8. Subtract the SpO2 signal from 1.0 9 . Cross-correlate the normalized Sp02 signal with the down-sampled and normalized ventilation signal 0. Find the offset to the maximum oross--orrelation attained 11. Calculate tne delayr in samples as the number of samples from the last index of the SpO2 signal Divide the delay in sample s by sampling rate to get the delay in seconds Optionally, as an alternative to the aforementioned squaring and square root operations being performed on the flow signal in steps I and 3 above, an absolute value operation on the loiw signal may be implemented. [0067] Fig. 8 shows a result of such a calculation by plotting a f i1 tered Sp02 signal as a function of time in seconds and the shifted ventilation signal usin the computed delay. Training a Classifier to Discriminate CSR [0068] The event feature and the Fourier-based spectral feature may be selected to train a classifier of the present technology. Training for an example embodiment was performed using 90 Emblet'ta recordings of clinical diagnostic studies. [0069] Two independent sets of po lysomnographic (P0) data were used for the development of the algorithm of a classifier. The first set (which is herein referred to as the EssenEmbla study) was a diagnostic clinical trial Conducted at - 17 a sleep facility in Essen, North-Rhine Westphalia, Germany, involving 90 patients presenting with Central Sleep Apnea (CSA) , OSA, and a combination of both. The EssenEmbia study was usec as the training set . The second set (BadO) was conducted in Bad Oeynhausen, North-Rhine Westphalia, Germany The prevalence of the BadO data set also contains recordings of CSA, OSA and a combination of both. These are 8 hours of overnight recordings that were then used as the test set- to evaluate the classifier after a training session. [0070] To facilitate the training of the algorithm of the classi liesr, initially bot-h sets of data had been pre classified by a clinician.. Each of the recordings were scored by the resident clinical expert at ResMed.in 30 minute segments, where a designation of predominant event is made by means of offline visual inspection through a computer with PSG software. The events were designated into one of five general types of events: 1. No apnea 2. CSR 3. OSA [Mixed apnea G. Combination of events [0071] As a result of this pre-classification process, each 8 hour recording yielded 16 non-overlapping epochs in total, each with a specified class of dominating event. in the EssenEmbla training set where 90 patients were involved, there was a total of 1440 classes of data available for training. Any residual epoch less than 30 minutes was not assessed. Nevertheless, the residual epoch may optionally be any period greater than several breath cycles of the patient. For example, the residual epoch may be greater than 5 minutes. The most preferred residual epoch may be 30 minutes. [0072] During this pre-scoring process, the clinical expert uilized any of the available PSG channel recordings to assist - 18 in determining the predominant events and assigning a designation to each of the halfI-hour segment . These included the nasal flow, digital oximetry, measures of respiratory effort, sleeping position by means of gravitational indictors, heart rate, el ectroencephalography (EEC) , electroocardiographv (ECG) , electromy ography (EMC) and electro-oculography (EOG) . Using the pre-classif ied designation of the training set, the oximetry and flow recording was segment ed with a computer processor and software into strict 30 minute non-overlapping epochs of data for analysis. Selected epochs of specific pre classified events were then used for exploring specific features to be used as indicators of CSR. By pre-classifving the data into half-hour epochs, the quantitative significance of particular short-term features was not diluted over he length off the entirerecording. [0073] The division of time for eaoh epoch was based upon living consideration to the typical occurrence and lengths of each CSR even: . For a higher than average 90 seconds oycle length of waxing and waning pattern of CSR, assuming the oxygen saturation de-- saturates and re-saturates at a similar pace, there are 20 continuous cycles of CSR that can be captured within half an hour, whioh was sufficient ror analys is. Acording to the American Academy of Sleep Medicine (AASM) 1999 published guideli nes for standards of P diagnostic criteria, mild obstructive s sleep apnea (OSA) defined as where on average between 5 to 15 events per hour of greater than 10 seconds cessation of breathing is found in a recording g. In a 30 minute epoch with the presence of mild OSA, there will be at minimum 2.5 events within half an hour. [0074] The decision boundary was formed using a Bayesian classification technigrue. l'his method is appropriate for normally distributed data and aims to find a discriminate that optimally separates the two c lasses (CSR and non-CSR) wit h minimum risks. Other classification methods may also be used to derive the decision boundary. SuCo examples may include neural networks or the k-nearest neighbor scheme. - 19 - [0075] Fig. 9 ill us t-rates the decision boundary and its relationship to the distribution of the data after training on an epoch---by-epoch basis. The straight ine represents the linear discriminant function and the elliptical line represents the quadratic discriminant function following Bayesian classification. The discriminant function divides the space into regions of CSR and non-CSR. [0076] Figs. 10 and 11 illustrate the trained decision boundary applied to the validation test data set on an epocn by-epoch basis. The overall probability for the entire SpO2 recording may be derived using the following series of steps. Calculate the probability by mapping the perpendicular distance to the decision boundary using the sigmoid f unct ion a P= ]+a' 2. If the proabil ity is greater than a spec ified threshold suC as 0.5, then the epoch will be classified as the CSR. 3. If any of the epochs are classified as CSR, the oximetric recording will be classified as CSR-probable [0077] Fig. 12 is a flow chart of example steps just described feature extraction and classification. Such a methodology may be implemented as software or in the circuits or memory of a detection device as further illustrated in FIG. 1. Patient-b--patent classification and results Probability Values [0078] To get an understanding of how well th e classifier discriminates CSR on a pat ient -by-pat ient basis, although it may be implemented to do so, instead of simply determining a binary output (CSR or non-CSR) for each epoch, the classifier may be implemented to produce a probability value of between zero and one for each epoch segment. For each derived mean resaturation duration and spectral feature, calculate the distance normal from the data point in the feature space to the decision boundary. This perpencicular distance is then mapped to a probability value where the probabi i ty is a function of the distance from the decision line. d e 1+e [0079] If the distance is zero i.e. (d=0) the feature value would coincide with the boundary, then the probability is exactly 0.5. As the distance increases to positive infinity, the probability asymptotically tends towards 1.0. As the distance increases to negative infinitV, the probability asymptotically tend towards 0.0. By defining the region of feature space corresponding to CSR as positive distance from the discriminant in this embodiment, CSR may be defined as any result nc probability value of greater than 0.5. It will b recognized that the technology may be implemented to yield other values for di's-tini guishing the presence of C SR with distance from such a discriminant function. [0080] In the process of classifying an oximetry recording on a patient-by-patient basis, a processor imp cemented algorithm embodying the classifier may be programmed to terate through the entire length of the signal, calculating a probability value for each -half hour epoc h, where the window increments by half an epoch per iteration (i.e. quarter of an hour) . The it erat-ion proceeds until all half-hour epochs have been processed and. a vector of probability values for the recording can be obtained. [0081] The overall probability of CS for a single patient / recording may be calculated using the maximum probability found for all- epcchs classified. The overall performance or the classifier then may be evaluated over the testing set by incorporating a threshold for the decision of CS. This may yield receiver-operating characteristics (ROC) such as the example depicted in Fig. 14.
    [0082] Each point in Fig. 14 on the ROC curve represents a 0.:5 increment / decrement of probability over its adjacent point . The maximum area is achieved at a threshold probability of 0.75 when sensi vC is 0.8148 and specificity 0.8571. By raisin the threshold probability further to 0.8, full specificity can be achieved at the expense of a lower sensitivity of 0.6667. The following table summarizes the key performance measures on a patient-by-patient basis: Threshold chosen (based on max area) . 75 Sensitivity 0.814815 Specificity .857143 Prior probability assume 0.004 Positive Predictive Value (PPV) 0.02069 Negative Predictive Value (NPV) 0.99883 False Alarm Rate (FAR) .97931 False Reassurance Rate (FRR) 00117 Positive Likelihood Ratio (LR+) 5.703704 Negative Likelihood Ratio (LR-) 0.216049 [0083] Note that this table assumes a prior probability of C.004 for patients with CS. This estimate is based on a prevalence of 0.01 of Americans with Congestive Heart Failure (CHF) whose age is over 65 years old reported in the Sleep Medicine Reviews (2006) 10, 33-47 by Jean-Louis Pepin etai Within the CHF population, a prevalence of one-third to one hal is commonly reported in literature on CSR. By takingthe prevalence value for CS within the CHF population as 0.4, the prior probability is calculated as 0.01 multiplied by 0.4, which equals 0.004. [0084] The positive likelihood ratio (TR+) indicates that if a patient is classified as CS positive overall, the ore-test probability of that pat ent truly having CS is boosted by a factor of 5.7 times. Similarly, the negative likelihood (LR-) if a patient is classified as CS negative overall, the pre test orobabil ity of that patient actually having CS is lowered -2 by a factor of 0.22. LR+ and LR- together indicate to the clinician, the strength of a diagnostic test. According to the rati rig on the qualitative strength of a diagnostic test by Dan Mayer in his book Essential Evidence-Based Medicine, an LR+ and LR- of 6 and 0.2 respectively is considered "very good" Thus, the diagnostic performance of the example classifier on a patient--bv-paient basis can be considered close to "very good" Application [0085] One application of such a classifier when implemented by a programmed processor or other processing device is to enable clinicians to screen a large number of patients for evidence of CSR as a comuter-aided diagnostic tool. One instance of such application may be used in the environment of come sleep testing, wh rein a sleep physician issues a portable SDB screening devil ce such as ApneaLinkT with an oximeter to a patient Preferably, sleep data may be collected ov ernight for subsequent analysis by the physician. This analysi s by the physician or clinician may be per formed offline, that is, after the use of the measuring device in one or more sleep sessions. For example, an algor i thm embodying the classifier can be implemented as a module for sleep study analysis software such as SomnologicaTM (manufactured by a company called Embla) or ApneaLinkTH (manufactured by ResMed Limited). This may allow the automatic scoring of CSR to be marked onan oximetry signal t-race or graph. An example embodiment is illustrated in the schematic of Fig. 13. A complementary feature would be a module that automatically generates a report' based on the classification results computed by the algorithm. Clinicians would then be able to use the report as a summary to support their decision-making process. Optionally, such a classifier algorithm may be implemented within an SDB screening device to generate data on a display message having a classification of CS as previous lY dliscussedi.
    [0086] Furthermore, in some embodiments, the aforement ioned oximetry classifier of the present technology may be used or implemente.d in conjunction with a flow rate c lass ifier, such as the flow rate classifier disclosed. in U.S. Patent App. Pub. No. 200R0177195, the entire disclosure of which is incorporated herein by reference. F or example, in such an embodiment, a controller with one or more programmed procesrs may include both an oximetry classifier algorithm and a flow rate classifier alcorithm. The flow -rate classifier may detect the delivered or measured flow rates and then analyze the flow rates with determinant functions and then classify the flow rat-es based on threshold amounts. A CS probability indicator generated by the controller may then be based on both classifier algorithms, for example, by combining the probability data from each, by using a scheme such as one based on the average of both probabilities or the maximum of either orobab-ility as the final conclusion draw- from both classifiers . Such a controller may have increased accuracy and generally better results. [0087] Accordingly, embodiments of the present technology may include a device or apparatus having one or more processors to implement Part icular CSR detection and/or training methodologies such as the classifies, thresholds, funct ions and/or algorithms described in more detail herein. Thus, the device or apparaLus may include integrated chips, a memory and/or other control instruction, data or ini ormaion storage medium. For example, programmed instructions encompassing such detection and/or training methodologies may be coded on integrated chips in the memory of the device or apparatus. Such instruction may also or alternatively be loaded as software or firmware using an appropriate data storage medium. With such a controller or- processor, the device can be used for processing data from an oximetry signal. Thus, the processor may control the assessment of a CSR occurrence or probabilit.y as described in the embodiments discussed in more detail hern. Moreover, in some embodiments, the device or t. 11 hl,: e Mo eo-r, apparatus itself may optionally be implemented with an oximeter or other blood gas measurement device to measure blood gas itself and then implement the methodologies discussed herein. In some embody ments, the processor control ins:ruciions may be contained in a computer readable recording medium as software efor use by a general purpose coruputer so that the general purpose computer may serve as a specific purpose computer according to any of the methodo logies discussed herein uron loading the software into the general purpose computer. [0088] An example embodiment is illustrated in FIG. 15. In the illustration, the CSR detection device 1501 or general purpose computer may include one or more processors 1508. The device may also include a display interface 1510 to output CS detection reports, results or graphs as described herein such as on a monitor or LCD panel. A user control/input interface 1512, for example, for a keyboard, mouse etc. may also be provided to activate the methodologies described herein. The device may also include a sensor or data interface 1514 for receiving data such as programming instructions, oximetry data, flow data, etc. The device may also typically include a. memory/data storage components. These may include processor control instructions for blood gas data/oximetry signal processing (e.g., re-processing methods, filters, wavelet transforms, FFT, delay calculations) at 1522 as discussed in more detail herein. They may also include procssor control instructions for classifier training methodologies at 1524. They may also include processor control instructions for CSR detection methodologies based on blood gas data and/or flow data (e.g., feature extraction methods, classification methods, etc.) at 1526. Finally, they may also iLncl ude stored data 1528 for these methodologies such as detected CSR events/probabilities, thresholds/discriminant functions, spectral features, event features, blood gas data/oximetery data, flow data, CSR report s, mean resaturation duration, resaturation. periods, etc. -25 [0089] While the technology has been described in connection with what are presently considered to be pract ical and preferred embodiments, it is to be understood that the technology is not to be limited to the disclosed emodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within he spirit and scope of the technology.
    权利要求:
    Claims (46)
    [1] 1. A method for indicating the presence of Cheyne Stokes respiration from blood oxygen saturation levels measured by an oximetry signal comprising: identifying and removing artifactual oximetry periods from the oximetry signal to produce a second signal; and with a processor, determining a mean length of contiguous periods of resaturation in the second signal and generating a positive indication of Cheyne-Stokes respiration based on an extent of the determined mean length.
    [2] 2. The method of claim 1 wherein the positive indication is generated when the extent of the determined mean length is greater than a predetermined threshold.
    [3] 3. The method of any one of the preceding claims, further comprising filtering the second signal to remove high frequencies with a filter.
    [4] 4. The method of claim 3, wherein the filter is a digital Finite Impulse Response (FIR) filter.
    [5] 5. The method of any one of claims 3 and 4, wherein the filter has a pass-band from 0 to 0.1 Hertz, a transition band from 0.1 to 0.125 Hertz and a stop band above 0.125 Hertz.
    [6] 6. The method of any one of claims 3-5, further comprising performing frequency analysis on the second signal to determine an extent of oscillation in the oxygen saturation level, and wherein a positive indication of Cheyne-Stokes respiration is generated for oscillations with cycle time lengths greater than a threshold.
    [7] 7. The method of any one of claims 3-5, wherein the second signal is Fourier analyzed to determine the extent of oscillation in the oxygen saturation level and wherein a -27- positive indication of Cheyne-Stokes respiration is generated for peaks in the Fourier-based spectrum at about 0.02Hz.
    [8] 8. The method of any one of claims 3-5, wherein the second signal is wavelet analyzed to determine the extent of oscillation in the oxygen saturation level and wherein a positive indication of Cheyne-Stokes respiration is generated for oscillations with cycle time lengths greater than a threshold.
    [9] 9. The method of any one of claims 1-8, wherein the artifactual oximetry periods are identified by detecting an initial sharp negative spike in the oximetry signal followed by an abrupt positive spike in the oximetry signal.
    [10] 10. The method of claim 9, wherein the negative spike has a value of less than -10% oxygen saturation and the positive spike has a positive value of greater than 10% oxygen saturation.
    [11] 11. The method of any one of claims 1-10, wherein the identified artifactual oximetry periods are removed by linearly interpolating across the regions of the oximetry signal comprising the identified artifactual oximetry periods.
    [12] 12. The method of any one of claims 1-11, further comprising counting the total number of oximetry signal samples measured in the oximetry signal over a period of time, determining the number of the oximetry signal samples from the measured oximetry signal over the period of time where the oxygen saturation level is below a predetermined threshold and calculating a quality indicator as a function of the number of oximetry signal samples below the predetermined threshold.
    [13] 13. The method of claim 12, wherein the quality indicator is determined as a ratio of the number of oximetry -28- signal samples below the predetermined threshold to the total number of oximetry signal samples measured.
    [14] 14. The method of any one of claims 12 and 13, wherein the predetermined threshold is a predetermined percentage threshold.
    [15] 15. The method of claim 14 wherein the predetermined percentage threshold is 10%.
    [16] 16. The method of any one of claims 1-15, further comprising receiving a ventilation flow signal data and determining the occurrence of periodic alternation between apnea or hypopnea and a hyperpnoea event.
    [17] 17. The method of claim 16, further comprising determining with the processor a delay of blood oxygen saturation level data compared to the occurrence of apnea or hypopnea and a hyperpnoea event; and generating a positive indication of Cheyne-Stokes respiration for determined delays above a predetermined threshold.
    [18] 18. A method for indicating the presence of Cheyne Stokes respiration from blood oxygen saturation levels measured by an oximetry signal and a ventilation flow signal comprising: determining with a processor a delay of blood oxygen saturation level data compared to ventilation flow level data having either an apnea or hypopnea and an hyperpnoea; and generating a positive indication of Cheyne-Stokes respiration for determined delays above a predetermined threshold. -29-
    [19] 19. A method for training a classifier to discriminate Cheyne-Stokes respiration from blood oxygen saturation levels measured by an oximetry signal comprising: pre-classifying polysomnographic data to obtain non overlapping epochs each with a specified class of dominating event; with a processor, segmenting oximetry and flow recordings into non-overlapping epochs of data having a predetermined length of time; and forming a decision boundary with a processor to discriminate Cheyne-Stokes respiration and non-Cheyne-Stokes respiration classes of events with the epochs.
    [20] 20. The method of claim 19 wherein the predetermined length of time is greater than five minutes.
    [21] 21. The method of claim 19, wherein the predetermined length of time is approximately 30 minutes.
    [22] 22. The method of any one of claims 19-21 further comprising: determining distance from the decision boundary for each event and normalizing the distance to a probability value for each epoch.
    [23] 23. The method of any one of claims 19-22, wherein the equal length epochs are as long as the recordings.
    [24] 24. The method of any one of claims 19-23, wherein the epoch length is approximately the length of a representative hypopnoea-hyperpnoea sequence.
    [25] 25. A device for detecting the presence of Cheyne-Stokes respiration from an oximetry signal and a ventilation flow signal, wherein said device identifies and removes artifactal oximetry periods from the oximetry signal to produce an second -30- signal; and wherein the device determines the mean length of contiguous periods of re-saturation in the second signal and returns a positive indication of Cheyne-Stokes respiration, if said mean length is greater than a predetermined threshold.
    [26] 26. The device of claim 25, wherein the device filters high frequencies from the second signal.
    [27] 27. The device of any one of claims 25-26, wherein said oximetry signal is compared to a first set of threshold values by a first classifier, and said ventilation flow signal is compared to a second set of threshold values by a second classifier.
    [28] 28. An apparatus for indicating the presence of Cheyne Stokes respiration from blood oxygen saturation levels measured by an oximetry signal comprising: means for identifying and removing artifactual oximetry periods from the oximetry signal to produce a second signal; and means for determining a mean length of contiguous periods of re-saturation in the second signal and generating a positive indication of Cheyne-Stokes respiration based on an extent of the determined mean length.
    [29] 29. An apparatus for indicating the presence of Cheyne Stokes respiration from blood oxygen saturation levels measured by an oximetry signal and a ventilation flow signal comprising: means for determining a delay of blood oxygen saturation level data compared to ventilation flow level data having either an apnea or hypopnea and an hyperpnoea; and means for generating a positive indication of Cheyne-Stokes respiration for determined delays above a predetermined threshold. -31-
    [30] 30. An apparatus for indicating the presence of Cheyne Stokes respiration from blood oxygen saturation levels measured by an oximetry signal comprising a processor configured to: receive the measured oximetry signal; identify and remove artifactual oximetry periods from the oximetry signal to produce a second signal; and determine a mean length of contiguous periods of resaturation in the second signal and generate a positive indication of Cheyne-Stokes respiration based on an extent of the determined mean length.
    [31] 31. The apparatus of claim 30 wherein the positive indication is generated when the extent of the determined mean length is greater than a predetermined threshold.
    [32] 32. The apparatus of any one of claims 30-31, wherein the apparatus is further configured to filter the second signal to remove high frequencies with a filter.
    [33] 33. The apparatus of claim 32, wherein the filter is a digital Finite Impulse Response (FIR) filter.
    [34] 34. The apparatus of any one of claims 32 or 33, wherein the filter has a pass-band from 0 to 0.1 Hertz, a transition band from 0.1 to 0.125 Hertz and a stop band above 0.125 Hertz.
    [35] 35. The apparatus of any one of claims 32-34, further comprising performing frequency analysis on the second signal to determine an extent of oscillation in the oxygen saturation level, and wherein a positive indication of Cheyne-Stokes respiration is generated for oscillations with cycle time lengths greater than a threshold. -32-
    [36] 36. The apparatus of any one of claims 32-34, wherein the second signal is Fourier analyzed to determine the extent of oscillation in the oxygen saturation level and wherein a positive indication of Cheyne-Stokes respiration is generated for peaks in the Fourier-based spectrum at about 0.02Hz.
    [37] 37. The apparatus of any one of claims 32-34, wherein the second signal is wavelet analyzed to determine the extent of oscillation in the oxygen saturation level and wherein a positive indication of Cheyne-Stokes respiration is generated for oscillations with cycle time lengths greater than a threshold.
    [38] 38. The apparatus of any one of claims 30-37, wherein the processor identifies the artifactual oximetry periods by detecting an initial sharp negative spike in the oximetry signal followed by an abrupt positive spike in the oximetry signal.
    [39] 39. The apparatus of claim 38, wherein the negative spike has a value of less than -10% oxygen saturation and the positive spike has a positive value of greater than 10% oxygen saturation.
    [40] 40. The apparatus of any one of claims 30-39, wherein the processor removes the identified artifactual oximetry periods by linearly interpolating across the regions of the oximetry signal comprising the identified artifactual oximetry periods.
    [41] 41. The apparatus of any one of claims 30-40, wherein the processor is further configured to count the total number of oximetry signal samples measured in the oximetry signal over a period of time, determine the number of the oximetry signal samples from the measured oximetry signal over the period of time where the oxygen saturation level is below a -33 - predetermined threshold and calculate a quality indicator as a function of the number of oximetry signal samples below the predetermined threshold.
    [42] 42. The apparatus of claim 41, wherein the quality indicator is determined as a ratio of the number of oximetry signal samples below the predetermined threshold to the total number of oximetry signal samples measured.
    [43] 43. The apparatus of any one of claims 41 or 42, wherein the predetermined threshold is a predetermined percentage threshold.
    [44] 44. The apparatus of claim 43 wherein the predetermined percentage threshold is 10%.
    [45] 45. The apparatus of any one of claims 30-44, further comprising a sensor to detect a ventilation flow signal and the processor is configured to determine the occurrence of periodic alternation between apnea or hypopnea and hyperpnoea events from the ventilation flow signal.
    [46] 46. The apparatus of claim 16, further comprising determining with the processor a delay of blood oxygen saturation level data compared to the occurrence of the apnea or hypopnea and hyperpnoea events; and generating a positive indication of Cheyne-Stokes respiration for determined delays above a predetermined threshold. -34-
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    同族专利:
    公开号 | 公开日
    AU2013221962B2|2016-02-11|
    AU2013221962C1|2016-12-01|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6529752B2|2001-01-17|2003-03-04|David T. Krausman|Sleep disorder breathing event counter|
    US7578793B2|2004-11-22|2009-08-25|Widemed Ltd.|Sleep staging based on cardio-respiratory signals|
    法律状态:
    2016-06-09| FGA| Letters patent sealed or granted (standard patent)|
    2016-09-08| DA2| Applications for amendment section 104|Free format text: THE NATURE OF THE AMENDMENT IS AS SHOWN IN THE STATEMENT(S) FILED 15 AUG 2016 . |
    2016-12-01| DA3| Amendments made section 104|Free format text: THE NATURE OF THE AMENDMENT IS AS SHOWN IN THE STATEMENT(S) FILED 15 AUG 2016 |
    2017-11-09| MK14| Patent ceased section 143(a) (annual fees not paid) or expired|
    优先权:
    申请号 | 申请日 | 专利标题
    US61/170,734||2009-04-20||
    AU2010239127A|AU2010239127B2|2009-04-20|2010-04-15|Discrimination of Cheyne -Stokes breathing patterns by use of oximetry signals|
    AU2013221962A|AU2013221962C1|2009-04-20|2013-08-29|Discrimination of Cheyne-Stokes Breathing Patterns by Use of Oximetry Signals|AU2013221962A| AU2013221962C1|2009-04-20|2013-08-29|Discrimination of Cheyne-Stokes Breathing Patterns by Use of Oximetry Signals|
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