A Novel Deep-Learning Method for Obstructive Sleep Apnea Detection from Single Channel Photoplethysmography

Early detection of obstructive sleep apnea (OSA) is extremely necessary to control its’ rising prevalence worldwide. Conventional diagnostic method like polysomnography (PSG) is uncomfortable, intrusive, and costly, thus, limiting its’ easy accessibility among people. To address this, we propose a novel deep learning method for detecting OSA using photoplethysmography (PPG) signal, a non-invasive method that is commonly available in wearable devices. We introduce a novel methodology using Multivariate Long Short-Term Memory-Fully Convolutional Network (MLSTM-FCN) model that effectively captures both temporal dependencies and local features in PPG signals for OSA detection. A new windowing technique was introduced to ensure apneic events are centered within each window to enhance the model’s ability to detect delayed physiological responses to apnea. The model was trained and evaluated on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset, achieving an improvement of 11.3% in accuracy over the state-of-the-art method. The method obtained an accuracy of 93.44%, precision of 0.94, recall of 0.91, and an F1-score of 0.93. These results demonstrate the potential of our method in accurately identifying OSA events. This method offers a unobtrusive, comfortable, and cost-effective alternative to traditional diagnostic tools, making it suitable for long-term, home-based monitoring.