A Single-Lead Electrocardiogram-Derivative Empirical Mode Decomposition-Based Deep Learning Model for Sleep Apnea Identification

While polysomnography (PSG) is the gold standard for detecting sleep apnea (SA), the insertion of several disruptive devices may impair the quality of the patient's sleep, and its interpretation requires specialised training from a sleep scientist or technician. Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used in recent years to automatically detect SA and lessen the negative effects of PSG. Currently, the majority of suggested methods concentrate on feature engineering and machine learning (ML) techniques, which call for previous expert knowledge and expertise. This paper uses a deep learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal to propose a SA detection method to distinguish between a normal and apnea occurrence. The EMD is the perfect tool for removing crucial elements that characterise the underlying physiological or biological processes. Based on 5- fold cross-validation (5fold-CV), the segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity. As a result, this work effectively created a unique and reliable SA detection system based on the ECG decomposed signal utilising EMD and deep CNN.