Yinxian He  Bokyung Amy Kwon  Kyungtae Kang

10.5626/JOK.2025.52.10.833

Deep Learning Hybrid Model Bi-LSTM ResNet Anomaly Detection Electrocardiogram

Recent advances in artificial intelligence (AI) have significantly enhanced classification tasks in medical domains, particularly in signal analysis, such as electrocardiograms (ECGs). This study focuses on detecting obstructive sleep apnea (OSA), a prevalent respiratory disorder, through signal analysis. OSA presents distinct yet irregular patterns in ECG signals, especially in the early stages, which pose challenges for detection using traditional methods or even deep learning approaches. To address these challenges, we propose a hybrid model that combines Bi-directional Long Short-Term Memory (Bi-LSTM) with a Residual Network (ResNet-18) specifically tailored for OSA detection. This model effectively leverages the abstract features and temporal dependencies of ECG signals to identify OSA patterns. Using ECG data from the PSG-Audio dataset, our approach achieved an accuracy of 94.65% (maximum 97.84%), precision of 94.65% (maximum 98.13%), sensitivity of 93.92% (maximum 100%), and an average F1 score of 0.94 (maximum 0.979), outperforming the Bi-LSTM-CNN combined model proposed in the previous study. Our findings demonstrate the potential of this model as a practical application for home-based health monitoring, providing an efficient, non-invasive solution for early OSA detection.