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 monitoring heart rhythm 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 single-lead ECG rhythm classification, termed CNN-Transformer Rhythm Classifier (CTRhythm), which integrates CNN with a Transformer encoder to capture local and global patterns effectively. CTRhythm achieved an overall F1 score of 0.831, outperforming the baseline deep learning models on the golden standard CINC2017 dataset. Moreover, pre-training 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.

The authors have declared no competing interest.

This study was funded by the Guangdong Basic and Applied Basic Research Foundation (2022A1515-011426), in part by the National Natural Science Foundation of China (61873027, 81970200, and 82271609), in part by the Guangzhou Municipal Science and Technology Project (2023B01J1011), and in part by the Shenzhen Science and Technology Program (JCYJ20190808100817047 and RCBS20200714114909234).

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