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    • 专利摘要:
    We present a method for the exploration of obstructive sleep apnea based on the oxygen saturation signal. With this method, an exploration or screening system is proposed that reduces the need to perform other more complex diagnostic tests such as polysomnography. These methods may be of interest to manufacturers of diagnostic devices and be applied in ambulatory settings. The method comprises a segmentation of the oxygen saturation signal, a process of extraction of variables in the time domain and frequency based on the variance of the signal and calculation of power in certain frequency bands. We propose a classification system based on logistic regression for the determination of the presence or absence of apnea in each segment of 1 minute. Fig. No. 1 presents the flow diagram of the method. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2684533A1
    申请号:ES201700500
    申请日:2017-03-30
    公开日:2018-10-03
    发明作者:Antonio Gabriel RAVELO GARCIA；Juan Luis NAVARRO MESA
    申请人:Universidad de las Palmas de Gran Canaria；
    IPC主号:
    专利说明:

    D E S C R I P C I Ó N

    METHOD FOR THE EXPLORATION OF OBSTRUCTIVE SLEEP APNEA BASED ON THE OXYGEN SATURATION SIGN
     5
    SECTOR OF THE TECHNIQUE

    Application Sector:
    Ambulatory sleep medicine
    Expert diagnostic systems 10
    Health and wellness monitoring systems
    Scientific or technical area:
    Sleep medicine
    Signal processing
    Data Mining 15
    Activity sector.
    Medical and technological sector

    BACKGROUND OF THE INVENTION
     twenty
    Apnea events occur as a result of the complete cessation of the inspiratory flow signal of at least 10 seconds duration. If the cessation manifests itself completely, it is an apnea and if it is partial, the obstruction is called hypopnea. These events characterize the Obstructive Sleep Apnea Syndrome (SAOS). 25
    In order to quantify the severity of OSA, the apnea-hypoapnea index (AHI) is defined, which indicates the number of apnea or hypoapnea events present during one hour of sleep. The American Academy of Sleep Medicine Task Force classifies SAOS as mild if the IAH is between 5 and 15, moderate if the IAH is between 15 and 30, and severe if the IAH is greater than 30. 30
    The OSA has direct consequences on health causing an alteration of the normal sleep architecture that can cause, among others, an increase in the risk of suffering cardiovascular problems. The SAOS is also considered a contributing factor to the increase in accidents.
    The Gold standard for the diagnosis of obstructive sleep panea is polysomnography (PSG), which is a technique based on a set of physiological signals collected from patients during sleep. Such as the electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), electrocardiogram (EKG), oxygen saturation, etc. Although these signals 5 constitute the reference for the diagnosis of sleep disorders, the application of this technique is expensive and tedious, requiring the registration of multiple signals and requiring qualified personnel to analyze them.
    Some devices measure what is called SpO2 by obtaining oxygen saturation values in peripheral areas such as the finger or earlobe. 10 Through pulse oximetry, the percentage of oxygen saturation of hemoglobin in blood can be measured non-invasively by means of photoelectric methods. Through the use of signal processing methods applied only to this signal it is possible to have a more comfortable screening system for the patient and low cost. fifteen
    Traditionally, SpO2 has been used to detect apnea events showing high specificity. For example using an oxygen desaturation index that accounts for the number of desaturations below a certain percentage with respect to a baseline value per hour. Other methods have used the measure of central tendency or different entropy techniques. A pattern classification approach can be applied to achieve greater accuracy in the OSA classification. Most of the proposals have tried to obtain a general measure that accounts for the degree of apnea of the subject but without considering the precise moment in which the events occur.
    Very few studies have considered the specific moment in which the 25 events occur. (Xie B. and Minn H. Real-time sleep apnea detection by classifier combination. Information Technology in Biomedicine, IEEE Transactions on, 16 (3): 469–477, 2012.) created a decision rule from three classifiers and A combination of 39 features. In (Ravelo A., Kraemer J., Navarro, J., Hernández E., Navarro J., Juliá G. and Wessel N. (2015). Oxygen Saturation and RR Intervals Feature Selection for 30 Sleep Apnea Detection. Entropy, 17 (5), 2932-2957.) A linear discriminate analysis was used to detect respiratory events from temporal and frequency variables of the SpO2 signal. In (Casanova U. (2014). Diagnostic system applied to the detection of obstructive sleep apnea by polygraphy)
    They used a total of 9 temporal and frequency variables of the SpO2 for event detection.
    The method presented in this document makes use of logistic regression as a classifier and of the variance and powers calculated in 6 frequency bands of SpO2 that make a simpler system of 7 variables feed a classifier of 5 logistic regression of low cost computational

    EXPLANATION OF THE INVENTION
    The method makes use of a segmentation of the SpO2 signal in one minute frames and performs an extraction of variables in each segment to compose a vector of characteristics formed by the variance of the signal on the one hand and by powers calculated from certain frequency bands obtained from the power spectral density by another. The calculation of the power spectral density is made from a five minute segment centered on the time of one minute that is to be analyzed. fifteen
    Before proceeding to calculate the power spectral density, it is necessary to eliminate the continuum term of the signal by subtracting its mean from the original signal. The periogram is used to calculate the spectrum of the signal using the Fourier transform (Eq 1).
                                                                            (Eq 1) 20 212/01 () () NjkNnSkSatneN
    To calculate the powers in the different bands a filtering is developed directly on the frequency domain. The following bands are considered for the calculation of the spectral powers:
    Band 2: 2.5 Hz - 5 Hz
    Band 3: 5 Hz - 7.5 Hz 25
    Band 8: 17.5 Hz - 20 Hz
    Band 10: 25 Hz - 27.5 Hz
    Band 12: 27.55 Hz - 30 Hz
    Band 20: 47.5 Hz - 50 Hz.
    The detection of an apnea event is determined from a model based on 30
    Logistic regression proposed to determine the probability of apnea from the characteristic vector that is extracted in each minute of the oxygen saturation signal. This probability can be determined from the following expression:

                                        01177 (...) 11SAOSxxPe  (Eq 2)
                                                                                                                            5
    Being β0 ... β7 the 8 parameters of the logistic regression model and x1 ... x7 the 7 variables analyzed in each minute. From a threshold, apnea is considered in a certain one-minute segment if the logistic regression value exceeds that value.
    BRIEF DESCRIPTION OF THE DRAWINGS 10
    FIG. 1. Flowchart of the method of detection of apnea events from the oxygen saturation signal.
    PREFERRED EMBODIMENT OF THE INVENTION
    The oxygen saturation signal is cut into segments of one minute. From this process, each of the segments is analyzed sequentially after the third period and feature vectors are generated with the concatenation of the SpO2 signal variance and the characteristics obtained from the calculation of power of the signal in specific frequency bands. Said spectral powers are obtained from segments of five minutes of SpO2 signal. After eliminating the continuous component of the signal by subtracting the average value from the signal, 20 the power is calculated in the following frequency bands: Second band between 2.5 Hz and 5 Hz, third band between 5 Hz and 7.5 Hz, band octave between 17.5 Hz and 20 Hz, eleventh band between 25 Hz and 27.5 Hz, twelfth band between 27.55 Hz and 30 Hz and twentieth band between 47.5 Hz and 50 Hz.
    The process of obtaining the feature vector per minute is repeated until all the segments of the register are analyzed.


    .
    权利要求:
    Claims (5)
    [1]
    1. A method for the exploration of obstructive sleep apnea based on the oximetry signal consisting of the following:
    to. Process the oxygen saturation signal to extract a variable in the time domain by time, the variance. 5
    b. Preparation of the oxygen saturation signal for the extraction of variables in the frequency domain by eliminating the continuous component of the signal and calculating the power spectral density.
    C. Process the oxygen saturation signal to extract a set of 10 variables in the frequency domain per time taken 5 minutes of signal around the time of interest.
    d. Detection of specific periods in which apnea events occur based on the oximetry signal and taking as variables the characteristics claimed in stages b and c. fifteen
    and. Obtaining an indicator of the severity of apnea from the oximetry signal quantifying the number of times in which the apnea event occurs.
    [2]
    2. The scanning method of claim 1 wherein it says oxygen saturation includes the oxygen saturation values obtained during sleep by any device that measures the level of hemoglobin saturation in blood.
    [3]
    3. The method of claim 1 wherein it says times refers to oxygen saturation signal sections of one minute duration.
    [4]
    4. The method of claim 1 wherein it says variables in the frequency domain is the calculation of the spectral power of the oxygen saturation signal in the second frequency band between 2.5 Hz and 5 Hz, third band between 5 Hz and 7.5 Hz, eighth band between 17.5 Hz and 20 Hz, eleventh band between 25 Hz and 27.5 Hz, twelfth band between 27.55 Hz and 30 Hz and twentieth band between 47.5 Hz and 50 Hz.
    [5]
    5. The method of claim 2 wherein said scanning method includes a classification method based on logistic regression.
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    同族专利:
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    引用文献:
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    US20140046209A1|2007-05-02|2014-02-13|Earlysense Ltd.|Monitoring, predicting and treating clinical episodes|
    US20140142452A1|2012-11-16|2014-05-22|University Of Manitoba|Acoustic system and methodology for identifying the risk of obstructive sleep apnea during wakefulness|
    KR101601895B1|2014-08-29|2016-03-22|연세대학교 원주산학협력단|Apparatus and method for automatic evaluation of apnea-hypopnea, reflecting sleep states and severity|
    JP2016214491A|2015-05-19|2016-12-22|国立大学法人京都大学|Apnea identification system and computer program|WO2021222897A3|2020-05-01|2021-12-09|The Brigham And Women's Hospital, Inc.|System and method for endo-phenotyping and risk stratfying obstructive sleep apnea|
    法律状态:
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    申请号 | 申请日 | 专利标题
    ES201700500A|ES2684533B2|2017-03-30|2017-03-30|Method for the examination of obstructive sleep apnea based on the oxygen saturation signal|ES201700500A| ES2684533B2|2017-03-30|2017-03-30|Method for the examination of obstructive sleep apnea based on the oxygen saturation signal|
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