Multiresolution Assessment of ECG Sensor Data for Sleep Apnea Detection Using Wide Neural Network

Sleep apnea (SA) is a noncommunicable medical condition associated with sleep, characterized by recurrent interruptions in breathing during sleep. Electrocardiogram (ECG) sensor data are commonly employed in medical diagnosis to capture the minute electrical impulses of the heart and effectively diagnose SA complications. Analyzing raw ECG data to extract the underlying subtle features poses challenges due to its nonlinear and nonstationary nature. This article introduces a novel approach to diagnosing SA patients using a multiresolution assessment of raw ECG data. The tunable <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factor wavelet transform (TQWT) is used to decompose the raw signal into multiple sub-bands (SBs). Selecting the optimal basis parameters for accurate decomposition using TQWT is a complex task. To address this, an artificial gorilla troop optimization algorithm is integrated with the TQWT method to determine the best tuning parameters, resulting in more representative SBs. To differentiate between apnea and normal ECG segments, an array of statistical features is evaluated from all SBs. Selected features are then used as input for five different neural network configurations and four other machine learning modules. The wide neural network (WNN) configuration achieves the highest accuracy of 96.60%, with a sensitivity of 96.50%, and specificity of 96.41%. This research proposes a nonparameterized technique for the efficient decomposition of nonstationary data. The presented strategy, in conjunction with the neural network configurations, can also be applied to detect other health complications.