A1 Journal article (refereed)
Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture (2023)

Liu, H., Cui, S., Zhao, X., & Cong, F. (2023). Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture. Biomedical Signal Processing and Control, 82, Article 104581. https://doi.org/10.1016/j.bspc.2023.104581

JYU authors or editors

Publication details

All authors or editors: Liu, Hang; Cui, Shaowei; Zhao, Xiaohui; Cong, Fengyu

Journal or series: Biomedical Signal Processing and Control

ISSN: 1746-8094

eISSN: 1746-8108

Publication year: 2023

Publication date: 23/01/2023

Volume: 82

Article number: 104581

Publisher: Elsevier BV

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.bspc.2023.104581

Research data link: https://github.com/ShaoweiCui/OSAdetect

Publication open access: Not open

Publication channel open access: 

Abstract

Obstructive sleep apnea (OSA) is a sleep breathing disorder that can seriously affect the health of patients. The manual diagnostic of OSA through the Polysomnography (PSG) recordings is time-consuming and tedious. Electrocardiogram (ECG) signals have been an alternative for OSA detection. This paper proposes a CNN-Transformer architecture for automatic OSA detection based on single-channel ECG signals. The proposed architecture has two fundamental parts. The first part has the aim of learning a feature representation from ECG signals by using the CNN. The second part consists mainly of the Transformer, a model structure built solely with self-attention mechanism, which is used to model the global temporal context and to perform classification tasks. The effectiveness of the proposed method was validated on Apnea-ECG dataset. The dataset consists of 70 ECG recordings with an annotation for each minute of each recording. The current and adjacent 1-min epochs were combined to form the 3-min input epoch. Besides, experiments were set up with different baseline deep learning models for sequence modeling to verify their effects on classification performance. The per-segment classification accuracy reached 88.2% and the area under the receiver operating characteristic curve (AUC) was 0.95. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 4.33. Experimental results demonstrate that the Transformer structure and a 3-min input time window both effectively improve the classification performance. The proposed method can accurately detect OSA from single-channel ECG signals and provides a promising and reliable solution for home portable detection of OSA.

Keywords: sleep apnea syndrome; diagnostics; ECG; signal analysis; signal processing; machine learning; deep learning; neural networks (information technology)

Free keywords: obstructive sleep apnea; ECG; transformer; deep learning

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 1