Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms

Abstract

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. However, existing approaches such as polysomnography, considered the most reliable and accurate test to detect sleep apnea, frequently require multichannel ECG recordings and advanced feature extraction algorithms, significantly restricting their wider application. Deep learning has recently emerged as a viable method for detecting sleep apnea. Our study describes a unique method for detecting sleep apnea utilizing single-lead ECG signals and deep learning techniques. In our proposed method, we have employed the continuous wavelet transform to convert electrocardiogram (ECG) signals into scalograms, which allows us to capture both the time and frequency domains. To enhance the classification performance, we have implemented an optimized pre-trained GoogLeNet architecture as a transfer learning model. In this study, we have analyzed the PhysioNet Apnea ECG dataset, UCDDB dataset and the MIT-BIH polysomnographic dataset for training and evaluation for per-segment classification, to demonstrate the effectiveness of our approach. In our experiments, the proposed model achieves remarkable results, with an accuracy of 93.85%, sensitivity of 93.42%, specificity of 94.30%, and F1 score of 93.83% for the Apnea ECG dataset in per-segment classification. Our model excels on the UCDDB dataset with 87.20% accuracy, 80.99% sensitivity, 93.39% specificity, and an 86.34% F1-score. Furthermore, the model obtains 88.58% accuracy, 88.78% sensitivity, 88.38% specificity, and 88.61% F1 score on the MIT BIH polysomnographic dataset, showing its robust performance and balanced precision–recall trade-off. Afterwards, LIME, an explainable AI method, has been implemented to illustrate the insights responsible for predicting apnea or non apnea.