Classification of sleep apnea based on EEG sub-band signal characteristics

Abstract

Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

An Automatic Classification Method of Sleep Apnea Events Based on EEG Frequency Sub-band Division

“…The proposed method outperformed the method in Monika et al In our previous work, the sample entropy and variance of the five EEG sub-band signals of delta, theta, alpha, beta, and gamma were extracted, then the optimal feature subset by the neighbor composition analysis (NCA) were applied to RF to classify the three types of events. The average accuracy of 88.99% was reported, in addition, the average accuracy of OSA events was 80.43%, CSA events was 84.85%, and normal breathing events was 95.24% [27]. In contrast, the average accuracy of the method used in this study increased by 1.44%, and most notably the classification accuracy of OSA events increased by 7.69%.…”

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

“…PSG can record respiratory signals (RS), electroencephalographic (EEG) data, electro-oculographic (EOG) data, electromyographic (EMG) data, electrocardiographic (ECG) signals, and pulse oximetry (PO) data [ 8 ]. In order to assist in the time-consuming scoring [ 9 ] of PSG records, efforts have been made to automate the detection of SAS [ 5 , 8 , 10 , 11 ]. At present, neural networks are among the most innovative and widely used classifiers in the field of biomedicine [ 12 ].…”

“…At present, neural networks are among the most innovative and widely used classifiers in the field of biomedicine [ 12 ]. There exist methods that can accurately distinguish sleep with apnea from physiological sleep [ 5 , 13 ], but the automatic detection of sleep apnea events is an open topic [ 11 ]. A recent study by Zhao et al [ 11 ] utilized a support vector machine (SVM) and a k -nearest neighbors ( k -NN) model for the apnea classification problem.…”

“…There exist methods that can accurately distinguish sleep with apnea from physiological sleep [ 5 , 13 ], but the automatic detection of sleep apnea events is an open topic [ 11 ]. A recent study by Zhao et al [ 11 ] utilized a support vector machine (SVM) and a k -nearest neighbors ( k -NN) model for the apnea classification problem. They also implemented a random forest (RF) model for data classification.…”

Eye State Identification Based on Discrete Wavelet Transforms

“…EEG is a popular technique for neuroimaging and brain signal acquisition widely used in the study of brain disorders [20] and in Brain-Computer Interface (BCI) systems [21].…”