Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea

Bozkurt, Ferda; Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit

doi:10.1016/j.irbm.2020.05.006

Cited by 38 publications

(32 citation statements)

References 26 publications

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“…In these cases, all the measures of performance, including sensitivity, specificity, and accuracy, have achieved values of more than 85%. These results outperform all the suggested methods to date [13,14,16,19,32,35]. The principal difference between the KNN and decision tree lies in their nature.…”

Section: Resultsmentioning

confidence: 74%

“…Apnea is detected based on nasal pressure signals with the help of convolutional neural networks (CNN) in [34]. Several supervised machine learning methods are tested for OSA detection with a single channel ECG signal in [35]. The achieved results are promising, yet in a small database and with slightly less accuracy than our suggested strategy.…”

Section: Introductionmentioning

confidence: 99%

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Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning and Classifier Combination for Home-Based Applications

Ghandeharioun¹

2021

Advances in Machine Learning

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Automatic detection of obstructive sleep apnea (OSA) is in great demand. OSA is one of the most prevalent diseases of the current century and established comorbidity to Covid-19. OSA is characterized by complete or relative breathing pauses during sleep. According to medical observations, if OSA remained unrecognized and un-treated, it may lead to physical and mental complications. The gold standard of scoring OSA severity is the time-consuming and expensive method of polysomnography (PSG). The idea of online home-based surveillance of OSA is welcome. It serves as an effective way for spurred detection and reference of patients to sleep clinics. In addition, it can perform automatic control of the therapeutic/assistive devices. In this paper, several configurations for online OSA detection are proposed. The best configuration uses both ECG and SpO2 signals for feature extraction and MI analysis for feature reduction. Various methods of supervised machine learning are exploited for classification. Finally, to reach the best result, the most successful classifiers in sensitivity and specificity are combined in groups of three members with four different combination methods. The proposed method has advantages like limited use of biological signals, automatic detection, online working scheme, and uniform and acceptable performance (over 85%) in all the employed databases. These advantages have not been integrated in previous published methods.

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Section: Resultsmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning and Classifier Combination for Home-Based Applications

Ghandeharioun¹

2021

Advances in Machine Learning

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“…We conducted this study based on three databases. The first two databases are public: St. Vincent, University College Dublin (UCD) database [36], eight subjects of Apnea-ECG database [37] whose data include more signals than one ECG channel. The third database is exclusively at our disposal and referred as "the Sina database" hereon.…”

Section: Methodsmentioning

confidence: 99%

Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals

Ghandeharioun¹

2021

IJAIA

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Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.

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“…The trend of automating apnea detection while simplifying measurements—such that the entire PSG is not required—has been applied at various levels [ 16 ], including the use of motion sensors outside the human body [ 17 ]. Overall, apnea detection research has been shifting towards the use of machine learning, which is more flexible for working with quasi-stationary biological data [ 18 , 19 ]. The disadvantage of some machine learning approaches is the need to extract features.…”

Section: Introductionmentioning

confidence: 99%

“…Sleep apnea is described by the apnea hypopnea index (AHI), which is the average number of apnea and hypopnea episodes per hour of sleep [5]. Classification according to the AHI uses four groups: Physiological standard (AHI < 5), mild sleep apnea (AHI < 15), moderate sleep apnea (AHI [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and severe sleep apnea (AHI > 30) [6]. About 2% of middle-aged women and 4% of middleaged men are affected by the apnea disease, and the initial examination of patients is carried out using a polysomnography system [7].…”

Section: Introductionmentioning

confidence: 99%

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

et al. 2021

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Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.

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Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea

Cited by 38 publications

References 26 publications

Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning and Classifier Combination for Home-Based Applications

Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning and Classifier Combination for Home-Based Applications

Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

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