Semi-Supervised Learning for Low-cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 11; pp. 1 - 12
• Main Authors: Hu, Shuaicong, Wang, Ya'nan, Liu, Jian, Yang, Cuiwei, Wang, Aiguo, Li, Kuanzheng, Liu, Wenxin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptability; Algorithms; Annotations; Apnea; Artificial neural networks; Auto-encoder (AE); Biological system modeling; Customization; Data models; Deep learning; EKG; Electrocardiogram (ECG); Electrocardiography; Feature extraction; Labels; Low cost; Machine learning; Neural networks; Obstructive Sleep Apnea (OSA); Recording; Semi-supervised; Semi-supervised learning; Sleep apnea; Sleep disorders
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3304299

Objective: Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an automatic semi-supervised algorithm for automated low-cost personalization fine-tuning. Methods: We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios. Results: By introducing semi-supervised personalization, the accuracy, AUC, and mean absolute error (MAE) of the general model (GM) of 35 subjects from the same database are improved from 86.3%, 0.915, and 5.178 to 90.3%, 0.948, and 2.593. Simultaneously, in the validation of 25 subjects from a cross-database, the accuracy, AUC, and MAE of the GM are enhanced from 75.6%, 0.800, and 9.149 to 84.3%, 0.881, and 3.509. Conclusion: The improved version of AE demonstrates excellent adaptability in identifying abnormal features in OSA, employing a data-driven approach to assign pseudo-labels for unknown data automatically. Additionally, leveraging the pseudo-labels through a semi-supervised fine-tuning strategy provides a solution to overcome the limitation of clinical annotations, facilitating low-cost implementation of personalized models. Significance: The semi-supervised approach proposed in this paper provides a high-performance and annotation-free solution for personalized adjustment of automatic OSA detection.