Key result
Emma-fApEn combined with random forest classification provided the highest accuracy in detecting obstructive sleep apnea from ECG recordings compared to SVM and KNN.
Why the study?
Obstructive sleep apnea is associated with autonomic nervous system dysfunction and reduced heart rate variability, and Emma-fApEn can analyze sympathetic tone over short periods during sleep.
Does Emma-fApEn combined with machine learning improve the accuracy of detecting obstructive sleep apnea from ECG recordings?
Does Emma-fApEn combined with machine learning improve the accuracy of detecting obstructive sleep apnea from ECG recordings?
Emma-fApEn combined with random forest classification can effectively detect obstructive sleep apnea from ECG recordings by analyzing the complexity of sympathetic tone.
Obstructive sleep apnea (OSA) is a common upper respiratory tract disease, which is related to autonomic nervous system (ANS) dysfunction and associated with reduced heart rate variability (HRV). Fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) can effectively analyze the physiological sympathetic tone in a short period of time during sleep. In this study, we compared fApEn-minima and fApEn-maxima obtained with Emma-fApEn with classic time-frequency domain indices using electrocardiogram(ECG) recordings from the PhysioNet database. The empirical results showed that Mean and LH could significantly differentiate OSA recordings from healthy recordings. Compared with support vector machine (SVM) and k-nearest neighbor classification (KNN), random forest (RF) provided the highest accuracy in OSA detection. Therefore, Emma-fApEn could analyze the decrease in the complexity of sympathetic tone in OSA patients during sleep.
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Wei et al. (2021) studied Obstructive sleep apnea (OSA). Fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) vs. Classic time-frequency domain indices, SVM, and KNN was evaluated on Accuracy in OSA detection. Emma-fApEn combined with random forest classification provided the highest accuracy in detecting obstructive sleep apnea from ECG recordings compared to SVM and KNN.
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