Classification of Sleep Apnea using Multi Scale Entropy on Electrocardiogram Signal

Abstract

One of sleep-disordered breathing (SDB) form is sleep apnea, commonly known as snoring during sleep, based on various complex mechanisms and predisposing factors. Sleep apnea is also related to various medical problems. It impacts morbidity and mortality so that it becomes a burden on public health services. Its detection needs to be done correctly through electrocardiogram signals to detect sleep apnea more quickly and precisely. This study was conducted to detect sleep apnea based on electrocardiogram signals using multi-scale entropy analysis. Multi-scale entropy (MSE) is used in a finite length of time series for measuring the complexity of the signal. MSE can be applied to both physical and physiological data sets and. In this paper we used MSE to detect Sleep Apnea on electrocardiogram (ECG) signals. MSE was applied two classes of ECG data, normal ECG signals, and apnea ECG signals. In this paper, classification and verification were carried out using the Support Vector Machine (SVM) and N-fold cross-validation (N-fold CV). From the experimental results, the highest accuracy was 85.6% using 5-fold CV and MSE scale of 10. The result shows that the system model that can detect sleep using the multi-scale entropy method

Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

“…5 presents a comparison between the proposed method and previous studies. Multiscale entropy produces the highest accuracy of 85.6% [16]. HRV used in other studies yielded the highest accuracy of 89.5%.…”

“…For this reason, several researchers use the morphology of the ECG signal as a feature for classifying normal and OSA ECG signals. The morphology of the ECG signal was measured using the entropy [16], the intrinsic mode function (IMF) of the ECG signal [17] [18], or signal fluctuations in the wavelet subband [19]. The use of EMD in the decomposition of the ECG signal for OSA detection has the potential to eliminate the information contained in the ECG signal.…”