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

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.