Atrial fibrillation detection by multi-scale convolutional neural networks

Abstract

Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-craft feature extraction. In this paper, A deep neural network named multi-scale convolutional neural networks (MCNN) based AF detector is proposed. Instant heart rate sequence is extracted from ECG signal, then an end-to-end MCNN detects AF with the instant heart rate sequence as input and detection result as output. The algorithm was tested on both public and private datasets. On the public dataset, with the sensitivity achieved being 0.9822, the corresponding specificity is 0.9811, and the overall accuracy is 0.9818. The area under an ROC curve is as high as 0.9962, compared to the AUC of the best conventional method is 0.9947. Comparison shows that the MCNN based AF detector give superior accuracy than conventional methods. Test on private dataset also shows significant improvement.