Build a bridge between ECG and EEG signals for atrial fibrillation diagnosis using AI methods

Moqing Li; Xinhua Zeng; Feng Wu; Yang Chu; Weiguo Wei; Min Fan; Chengxin Pang; Xing Hu

doi:10.1016/j.compbiomed.2023.107429

Build a bridge between ECG and EEG signals for atrial fibrillation diagnosis using AI methods

Comput Biol Med. 2023 Sep 11:166:107429. doi: 10.1016/j.compbiomed.2023.107429. Online ahead of print.

Authors

Moqing Li¹, Xinhua Zeng², Feng Wu³, Yang Chu⁴, Weiguo Wei⁵, Min Fan⁶, Chengxin Pang⁷, Xing Hu⁸

Affiliations

¹ Academy for Engineering and Technology, Fudan University, No. 220, Handan Rd, Yangpu District, Shanghai, 200433, China. Electronic address: mqli21@m.fudan.edu.cn.
² Academy for Engineering and Technology, Fudan University, No. 220, Handan Rd, Yangpu District, Shanghai, 200433, China. Electronic address: zengxh@fudan.edu.cn.
³ Department of Cardiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No. 110, Ganhe Rd, Hongkou District, Shanghai, 200437, China. Electronic address: wufengmed@163.com.
⁴ Department of Cardiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No. 110, Ganhe Rd, Hongkou District, Shanghai, 200437, China. Electronic address: charrylin2003@outlook.com.
⁵ Department of Cardiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No. 110, Ganhe Rd, Hongkou District, Shanghai, 200437, China. Electronic address: 17777236157@163.com.
⁶ Department of Cardiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No. 110, Ganhe Rd, Hongkou District, Shanghai, 200437, China. Electronic address: fanminyueyang@outlook.com.
⁷ School of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Rd, Pudong New Area, Shanghai, 201306, China. Electronic address: chengxin.pang@shiep.edu.cn.
⁸ Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, No. 516, Jungong Rd, Yangpu District, Shanghai, 200093, China. Electronic address: huxing@usst.edu.cn.

PMID: 37734354
DOI: 10.1016/j.compbiomed.2023.107429

Abstract

Atrial fibrillation (AF) is a very common type of cardiac arrhythmia. The main characteristic of AF is an abnormally rapid and disordered atrial rhythm causing an atrial dysfunction, which can be visualized on an electrocardiograph (ECG) and distinguished by irregular fluctuations. Despite continuous and considerable efforts to analyze the pathophysiology of AF, it is challenging to determine the underlying pathogenesis of the disease in individual patients. This study aims to build a bridge between ECG and electroencephalogram (EEG) signals to probe the strong influence between human brain activity and AF by AI methods. We first found that the one-second data fragment shows the most excellent performance in our time window configuration. Secondly, in our proposed measurement, most cortical potentials were partly associated with AF. Thirdly, we found that only a few channels of data were sufficient for analysis. Finally, our experiment shows δ wave has the best performance compared to other wave bands. By AI methods, the paper contributes to concluding that δ wave band of EEG is the most associated brain wave type with AF. By EEG signals from typical regions, the central region, parietal and Occipital might be the most associated encephalic regions with AF. The clinical trial registration number for our study is ChiCTR2300068625.

Keywords: AI methods; Atrial fibrillation; Electroencephalogram.