Identifying episodes of sleep apnea in ECG by machine learning methods

Ivanko, K.; Ivanushkina, N.; Rykhalska, Anna Kostiantynivna

doi:10.1109/elnano50318.2020.9088749

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Замість використання 12 фізіологічних сигналів полісомнограми з метою зменшення обчислювальних витрат для виявлення апное сну також використовується аналіз лише одного з сигналів. Дослідження довели можливість виявлення апное сну за допомогою ознак, отриманих на основі ЕКГ сигналу, а саме показників варіабельності ритму серця (ВРС) [7][8][9][10][11][12][13][14]. Також ряд наукових робіт присвячено дослідженню можливості виявлення апное на основі аналізу ЕЕГ сигналів [15][16][17][18].…”

Section: вступunclassified

“…Дані розділено на наступні класи: клас А (апное)записи, що містять не менше 100 хвилин з випадками апное; клас B (граничний з апное) -записи, що містять від 5 до 99 хвилин з випадками апное; клас C (контрольний) -записи, що містять менше 5 хвилин з апное і можуть вважатися нормою. "The Apnea-ECG Database" була використана нами в попередньому дослідженні [14], щоб оцінити ефективність методів машинного навчання для виявлення епізодів апное сну на основі показників варіабельності ритму серця (ВРС), розрахованих за сигналом ЕКГ. Було досліджено показники, розраховані у часовій і частотній областях, а також показники, отримані за допомогою спектрально-часового та вейвлет-аналізу.…”

Section: матеріали і методиunclassified

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Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

Rykhalska

Ivanko

Ivanushkina

et al. 2022

Microsyst., Electron. & Acoust.

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The article is devoted to the application of machine learning methods for computerized detection of sleep apnea episodes based on the analysis of single-channel signals of the electrocardiogram (ECG) and electroencephalogram (EEG). To study the possibilities of machine learning to detect apnea based on ECG and EEG analysis, we used Apnea-ECG database and MIT-BIH polysomnographic database from PhysioNet, which contain annotations to each minute of records indicating the presence or absence of apnea/hypopnea at the current time. In order to apply machine learning methods to the problem of automated detection of sleep apnea/hypopnea episodes in ECG and EEG signals, long-term polysomnograms available in MIT-BIH polysomnographic database were segmented according to annotations into shorter sections lasting 30 seconds each. The study used 267 segments lasting 30 seconds for the class "norm", 258 segments for the class "apnea" and 273 segments for the class "hypopnea", a total of 798 simultaneous ECG and EEG recordings. The aim of this work is to identify and compare informative signs of sleep apnea episodes in terms of heart rate variability (HRV) and brain electrical activity, as well as the choice of classification methods that provide the highest accuracy for this task. Features of cardiorhythmograms in time and frequency domains, spectral-temporal and wavelet characteristics, as well as parameters of EEG signals based on energy ratio of EEG rhythms, Hearst index, Higuchi fractal dimension and sample entropy for EEG signals are considered. Using different sets of features, the accuracy of classifiers based on decision trees, discriminant analysis, support vector machines, k-nearest neighbor method, and ensemble training was determined. Based on this, combination of features and classifiers is proposed, which provides the highest accuracy of recognition of sleep apnea episodes according to single-channel ECG and EEG signals, taken separately and in the case of a combination of their features. The best results of classification of signals "norm", "apnea" and "hypopnea" were obtained for the model trained using weighted method k nearest neighbors with 25 features of HRV: the total percentage of correctly identified cases for three classes was 99.9% (797 correctly identified cases of 798). By reducing the number of HRV parameters to 9, the best machine learning result was achieved using the bagging ensemble algorithm with 30 decision trees: the total percentage of correctly identified cases for all three classes was 99.4% (793 correctly identified cases from 798: for "norm" - 265 cases from 267, for "apnea" - 257 cases from 258, for "hypopnea" - 271 cases from 273). The use of EEG parameters as features for apnea/hypopnea recognition showed worse results compared to HRV parameters. In this case, the best result of machine learning was achieved using support vector machines with quadratic kernel function: the total percentage of correctly identified cases for three classes was 91.9% and the signals corresponding to norm were most badly recognized (27 cases were classified as hypopnea, and in 9 cases - as sleep apnea). The combination of HRV and EEG parameters gave the best accuracy of 99.1%, but the results are comparable to using only HRV parameters. The obtained results indicate that HRV parameters allow recognizing sleep apnea and hypopnea with higher accuracy than EEG parameters, but EEG signal undoubtedly reflects signs of sleep apnea/hypopnea and also can be used for apnea recognition.

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Section: вступunclassified

Section: матеріали і методиunclassified

Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

Rykhalska

Ivanko

Ivanushkina

et al. 2022

Microsyst., Electron. & Acoust.

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“…The classifier algorithm used was multiple logistic discrimination. In [ 35 ], 24 time and frequency domain features are extracted from ECG signals. This included time domain features such as mean, median, standard deviation, and mode for each NN interval series, and frequency domain features such as normalized power in various frequency ranges, and the vegetative balance index.…”

Section: Machine Learning In Sleep Apnea Detection Based On Biomedical Markers In Wearable Devicesmentioning

confidence: 99%

“… - [ 23 ] 2017 52 subjects SPO2 Linear Discriminant Analysis Features from SPO2 (such as number of desaturations > 3%, spread of SPO2, minimum and average of SPO2), PPG and PPG derived respiration were extracted for classifier training. Accuracy: 87% [ 35 ] 2020 Apnea-ECG @ ECG Decision trees, DA, logistic regression, SVM, kNN, ensemble learning 24 time and frequency domain features were extracted. Feature selection by discarding redundant features, which resulted in a set of 9 features for classifier training Accuracy: 98.7% [ 52 ] 2020 Not specified Respiration, SpO2, heartrate, 3-ACC signals Gaussian Naïve-Bayes, ANN, kNN Dataset was collected and labelled per AASM’s sleep apnea judgement criteria.…”

Section: Table A1mentioning

confidence: 99%

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Ramachandran

Karuppiah

2021

Healthcare

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Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

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“…Electrocardiogram (ECG) is one of the most reliable physiological signals containing information about central cardiovascular function, respiration, and electrical activity of heart. For this reason, we see several studies on AHI index screening by using machine learning and deep learning methods as in Banluesombatkul, Rakthanmanon & Wilaiprasitporn (2018) , Ivanko, Ivanushkina & Rykhalska (2020) , Li et al (2018) , Erdenebayar et al (2019) . Many patients with OSA report a history of shortness of breath associated with hypopnea.…”

Section: Introductionmentioning

confidence: 99%

Sleep disorder and apnea events detection framework with high performance using two-tier learning model design

Arslan

2023

PeerJ Computer Science

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Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.

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Identifying episodes of sleep apnea in ECG by machine learning methods

Cited by 9 publications

References 14 publications

Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

Detection of Episodes of Sleep Apnea and Hypopnea in ECG and EEG Signals by Machine Learning

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Sleep disorder and apnea events detection framework with high performance using two-tier learning model design

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