A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals

• Published in: Computer methods in biomechanics and biomedical engineering Vol. 28; no. 14; pp. 2137 - 2149
• Main Author: Gupta, Kapil
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.11.2025; Taylor & Francis Ltd
• Subjects: Accuracy; Algorithms; Apnea; Artificial neural networks; Cognition; Deep Learning; EKG; Electrocardiography; electrocardiography (ECG); Humans; Machine learning; Male; Middle Aged; Neural networks; Neural Networks, Computer; Parameters; Signal Processing, Computer-Assisted; Sleep apnea; sleep apnea (SA); Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Sleep disorders; T-F analysis; Deep learning; sleep apnea (SA); T-F analysis; electrocardiography (ECG)
• ISSN: 1025-5842; 1476-8259; 1476-8259
• DOI: 10.1080/10255842.2024.2359635

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.