Atrial Fibrillation Detection in Spectrogram Based on Convolution Neural Networks

Nowadays, the computerized electrocardiogram (ECG) interpretation is the best available tool for the detection of heart diseases. The symptoms of atrial fibrillation, one of the heart disease, are the most challenging task of heart disease that relies on the cardiologist to read the ECG. However, this task involves a time-consuming process, leading to fatigue-induced medical errors. In this paper, a novel atrial fibrillation diagnostic algorithm based on deep learning architecture is proposed which incorporates the spectrogram to further improve the accuracy performance. As shown in the experimental results, the proposed method on PhysioNet /CinC Challenge 2017, which contains 8528 of single-lead ECG recordings, achieves the 10-fold cross-validation set performance of 78%.