A photoplethysmography-based diagnostic support system for obstructive sleep apnea using deep learning approaches. (September 2022)

• Record Type: Journal Article
• Authors: Jothi, E. Smily Jeya; Anitha, J.; Hemanth, D. Jude
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Obstructive sleep apnea -- Deep learning -- Photoplethysmogram -- Temporal convolutional network -- Bi-directional long short-term memory -- TCN-LSTM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108279
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Sleep disorders such as OSA are common. Deep Learning techniques are used to detect OSA automatically. Networks are trained using PPG signals from 1375 subjects. Three different deep learning techniques are used, of which TCN-LSTM exhibits promising results. Real-time OSA event analysis is possible with this model. Abstract: Obstructive Sleep Apnea (OSA) is a common sleep disorder characterized by periods of reduced or complete cessation of airflow during sleep due to obstruction of the upper respiratory pathway. A novel deep learning framework is developed for automated feature extraction and detection of OSA events from Photoplethysmogram (PPG) signals recorded at the finger tip of the subjects using a Photoplethysmography sensor. This helps in real-time automatic OSA screening at a faster rate and reduces the need for an exhausting and time-consuming Polysomnography (PSG) sleep study. Bi-directional Long Short-Term Memory (Bi-LSTM), Temporal Convolutional Network (TCN), and TCN-LSTM are the three deep learning approaches implemented to facilitate the automatic screening of OSA events, and their performance is compared. Training and testing are carried out using datasets collected from Physionet's apnea database and real time PPG signals of 315 subjects from diverse age groups with health conditions viz., hypertension, cardiovascular disease, and OSA. The performance of TCN-LSTM is better compared to the performance of TCN and Bi-LSTM. The proposed system exhibits an accuracy of 93.39%, a specificity of 94.37%, a sensitivity of 98.98% and F1 Score of 94.12%. Graphical abstract: Image, graphical abstract