CTRhythm: Accurate Atrial Fibrillation Detection from Single-Lead ECG by Convolutional Neural Network and Transformer Integration

Atrial Fibrillation (AF) is a common supraventricular arrhythmia that affects about 30 million people globally. Electrocardiogram (ECG) analysis is the primary diagnostic approach. The widespread adoption of wearable devices for heart rhythm monitoring prompted the development of AF detection models for single-lead ECGs, benefitting real-time early diagnosis. Current state-of-the-art methods for AF detection are convolutional neural network (CNN) and convolutional recurrent neural network (CRNN) based models, which only focus on capturing local patterns despite heart rhythms exhibiting rich long-range dependencies. To address this limitation, we propose a novel method for singlelead ECG rhythm classification, termed CNN-Transformer Rhythm Classifier (CTRhythm), which integrates CNN with a Transformer encoder to effectively capture both local and global patterns effectively. CTRhythm achieved an overall F1 score of 0.831, outperforming baseline deep learning models on the gold-standard CINC2017 dataset. Furthermore, pretraining with additional data improved the overall F1 score to 0.840. In two external validation datasets, CTRhythm showed its strong generalization capabilities. CTRhythm is freely available at https://github.com/labxscut/CTRhythm.