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

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...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2023.3304299