A Novel Methodology for Obstructive Sleep Apnea Detection from ECG using Deep Learning Approach

Obstructive sleep apnea (OSA) has become a serious health concern with increasing morbidity worldwide. Even though polysomnography is widely used by physicians for diagnosing OSA, the process is costly, time-consuming, and uncomfortable for patients. This increases the demand for developing unobtrusive, cost-effective, and reliable solutions for detecting OSA and reducing patient discomfort. Several machine learning-based and some deep learning based methods using extracted ECG features for OSA detection from ECG are found to be less reliable due to the manual feature extraction process, very few studies(included in the comparison table of below section) have used only deep learning methods for OSA detection from ECG signals. In this study, we proposed a novel deep learning method that leverages convolutional neural networks (CNN) and long short-term memory (LSTM) networks to learn spatial and temporal features for detecting OSA from ECG data. Our model was trained and evaluated on the publicly available MESA and Apnea-ECG datasets to assess its robustness. In a prudent data windowing step, we center the apnea data within each data window, enabling the model to better learn apnea patterns and resulting in achieving an accuracy of 95.4%, 92.2%, Specificity of 95.1%, 92.8%, Sensitivity of 94.8%, 91.6% and F1 score of 94.1%, 92.1% respectively on MESA and Apnea-ECG dataset. Our model yields an increase of 0.57% and 9.31% in specificity, 1.87% and 50.47% in sensitivity, 4.89% in F1-score, and 3.47% and 17.77% in accuracy against the best result we found using the Apnea-ECG and MESA dataset respectively. The results show our proposed model outperforms state-of-the-art methods on the MESA dataset and achieves equally good results on the Apnea-ECG dataset. Implementation of our model on Jetson orin AGX board gains comparable results with the above-stated accuracy showing the possible practical use of our methodology. These findings highlight the model’s stability, robustness, and high accuracy in detecting OSA.