Self-supervised learning for atrial fibrillation detection with ECG using CNNTransformer

Cardiovascular diseases are a significant cause of mortality worldwide, and the accurate diagnosis of these conditions is essential for effective treatment and management. Electrocardiograms (ECGs) are a common diagnostic tool used by cardiologists, but the manual interpretation of ECGs can be relatively time-consuming and challenging, particularly in cases of atrial fibrillation (AF), which is associated with an increased risk of stroke, heart failure, and other complications. To address the need for reliable and automatic ECG classifiers, we propose a new method using self-supervised learning with a CNNTransformer architecture to improve the ECG classification performance. The proposed model is pre-trained on the China Physiological Signal Challenge 2018 dataset and part of the Physikalisch-Technische Bundesanstalt (PTB) XL dataset using a novel 'nextclip' prediction task, which asks the model to predict the next small segment of ECG, followed by finetuning on the ECG classification task. Our experimental results demonstrate that our proposed method achieves state-of-the-art results for ECG classification, with an average F1-score of 0.84 and 0.96 for AF detection on the CPSC2018 dataset. The proposed CNNTransformer architecture has shown to be an effective and efficient solution for ECG classification, especially on AF.