Abstract
A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether the reduction of lower frequency P and T waves can increase the accuracy of the detection of apnea events. This study proposed filter bank decomposition to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal. One-minute ECG signals obtained from the MIT PhysioNet Apnea-ECG database were used to train the CNN models and test the accuracy of detecting apnea events for different subbands. The results show that the use of the newly selected subject-independent datasets can avoid the overestimation of the accuracy of the apnea event detection and can test the difference in the accuracy of different subbands. The frequency band of 31.25–37.5 Hz can achieve 100% per-recording accuracy with 85.8% per-minute accuracy using the newly selected subject-independent datasets and is recommended as a promising subband of ECG signals that can cooperate with the proposed 1D CNN model for the diagnosis of OSA.
Highlights
Accepted: 8 January 2022Obstructive sleep apnea (OSA) is characterized by repeated collapse of the upper airway during sleep
This study proposed filter bank decomposition with Butterworth bandpass filters to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal
The main contribution of this study is that it proposes filter bank decomposition to decompose the ECG signal into 15 subband signals, and a 1D CNN model independently cooperated with each subband to evaluate the accuracies of different subbands for the detection of apnea events
Summary
Obstructive sleep apnea (OSA) is characterized by repeated collapse of the upper airway during sleep. It blocks the airway and causes shallow and laborious breathing [1]. OSA is very common in patients with cardiovascular disease and is associated with an increased incidence of stroke, heart failure, atrial fibrillation, and coronary heart disease. Severe OSA is further associated with increased all-cause and cardiovascular mortality [2]. OSA affects approximately 9–24% of the general population, but the number of patients who have been diagnosed is very limited, and about 90% of sufferers are still undiagnosed [3]. Early diagnosis and treatment of OSA can reduce adverse human health conditions
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