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

Hidefumi Kamozawa, Sho Muroga, Motoshi Tanaka

研究成果: ジャーナルへの寄稿学術論文査読

5 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)577-582
ページ数6
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
18
4
DOI
出版ステータス出版済み - 2023 4月

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