Noncontact capacitive coupling ECG-Derived respiratory signals using the conformer based time–frequency domain generative adversarial network

• Published in: Expert systems with applications Vol. 289; p. 128360
• Main Authors: Xiao, Zhijun, Vos, Maarten De, Chatzichristos, Christos, Dong, Kejun, Jiang, Yunyi, Wang, Zhongyu, Zhang, Yuwei, Ding, Fei, Yang, Chenxi, Li, Jianqing, Liu, Chengyu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2025
• Subjects: Bedside Respiratory Monitoring; Capacitive coupling electrocardiogram; Generative Adversarial Network; Time-Frequency Domain Enhancement; Capacitive coupling electrocardiogram; Time-Frequency Domain Enhancement; Bedside Respiratory Monitoring; Generative Adversarial Network
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2025.128360

Respiratory monitoring and analysis is a key method for detecting sleep-related diseases. This paper presents a novel approach for respiratory monitoring that utilizes noncontact capacitive coupling electrocardiograms-derived respiration (cEDR) method. We propose a Time-Frequency Domain Generative Adversarial Network (TF-GAN) method for generating respiratory signals, and successfully apply it to capacitive coupling electrocardiograms(cECG). First, we analyze the mechanism of respiratory coupling with cECG and verify the feasibility of the theory. Then, using the developed device, we collect cECG data from 16 subjects during the night and simultaneously collect respiratory signals as a reference, to validate the feasibility of our approach. Next, we convert the collected cECG data into time–frequency domain features using Short-Time Fourier Transform (STFT) and input these features into a Convolution-augmented transformer (Conformer) based Generative Adversarial Network(GAN) to generate the cEDR. The network architecture integrates self-attention mechanisms and time–frequency domain enhancement mechanisms to effectively extract the respiratory energy components. Finally, we compare the generated respiratory signals with the reference signals. The experimental results show that the generated respiratory signals exhibit a high correlation with the reference signals. Specifically, 86.3 % of the signals have a absolute waveform correlation coefficient greater than 0.5, indicating good reproduction of real breathing waveforms. Our proposed model demonstrates superior performance in respiratory signal extraction, achieving a low Root Mean Square Error (RMSE) of 0.96 ± 0.12 bpm and a high agreement rate of 94.83 % ± 0.30 % within the Bland–Altman limits. Additionally, the model maintains an effective respiratory segment ratio of 67.56 % ± 8.89 %, even under poor cECG signal conditions, showcasing its robustness and reliability.