WASPCN-Net: Automatic Detection of Obstructive Sleep Apnea Using Smoothed Wavelet Spectrograms of Single-Lead ECG Signals

Obstructive sleep apnea (OSA) is an extremely severe condition. At present, the conventional polysomnography (PSG) test is utilized to treat OSA using several physiological signals, such as electroencephalogram (EEG), electrocardio-gram (ECG), and the oxygen level in their blood. During the PSG test, the patient is required to wear many sensors while sleeping. This method is unquestionably complex, expensive, and can cause discomfort to the patients. In addition, it is more appropriate to use single-lead ECG signals for wearable mobile devices due to their compatibility with noninvasive requirements and hardware limitations. In this research, a deep learning model (DLM) using smoothed wavelet spectrograms (SWS) of ECG signals is proposed for the automatic classi-fication of OSA. The SWSs are smoothened using Savitzky-Golay (S-G) filter. Then, these SWSs are provided as input to the designed DLM called WAvelet SPectrogram-based Convo-lutional Neural Network (WASPCN-Net) and pre-trained Res-Net50 model. The WASPCN-Net model obtained an accuracy of 87.25%, sensitivity of 78.97%, and specificity of 92.35% with SWS using a 10-fold cross-validation approach, which is superior to many state-of-the-art techniques. Further, we also obtained the performance on the pre-trained ResNet-50 model and received an accuracy of 86.92%, sensitivity of 81.74%, and specificity of 90.12%. The proposed WASPCN-Net is more accurate, simple, fast, and robust than the Res-Net50 model because it requires relatively few tunable learning parameters.