A Detection Method of Atrial Fibrillation from 24‐hour Holter‐ECG Using CNN

A method for detecting atrial fibrillation (AF) from electrocardiogram (ECG) measured by a 24‐hour Holter electrocardiograph (Holter‐ECG) is proposed using convolutional neural network (CNN). In the preprocessing stage, artifacts and noises on Holter‐ECG are removed by a bandpass filter. The detection method consists of two stages: extraction of abnormal waveforms using one‐dimensional CNN trained with segmented ECG waveform and its spectral entropy, and identification of AF using two‐dimensional CNN trained with segmented ECG spectrogram. A total of 47 520 datasets obtained from Holter‐ECG were prepared, and used for training at both CNN stages. Newly prepared (untrained) datasets of 24‐hour Holter‐ECG of 10 subjects and MIT‐BIH databases are tested, and the proposed method showed sufficient performance for detecting AF, with the accuracy of approximately 90%. This result indicates the feasibility of the proposed method.