Advanced Biomedical Engineering
Online ISSN : 2187-5219
ISSN-L : 2187-5219
Detection of Atrial Fibrillation from Holter ECG using 1D Convolutional Neural Network after Arrhythmia Extraction
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2024 Volume 13 Pages 19-25


Atrial fibrillation (AF) is a type of arrhythmia that can cause cardiac complications such as stroke, and early detection is therefore important. This study proposes a method for detecting AF from the Holter electrocardiogram (ECG). The AF detection procedure has two stages: arrhythmia extraction based on the R-R interval variation and AF identification from the extracted arrhythmia using a one-dimensional convolutional neural network (1D CNN). Artifacts in the ECG are eliminated through preprocessing using a finite-impulse-response bandpass filter. In the first stage, R waves are detected through a multi-resolution analysis of the ECG, and arrhythmias are extracted by observing the standard deviation of the R-R intervals. In the second stage, AF is identified from the extracted arrhythmic events using a 1D CNN trained using segmented ECG waveforms. An ECG dataset of 100,000 segments obtained from the Holter ECG is prepared for training the CNN. Evaluation using 24-h ECG data from 10 untrained subjects verifies that the performance of the proposed detection method is better than that of the methods without arrhythmia extraction, with an accuracy of 93.1%. This result indicates the feasibility of the proposed method for detecting AF.

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© 2024 Japanese Society for Medical and Biological Engineering

Copyright: ©2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons BY 4.0 International (Attribution) License (, which permits the unrestricted distribution, reproduction and use of the article provided the original source and authors are credited.
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