research-article

Atrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG

Authors:

Baofeng ZhuAuthors Info & Claims

ICGSP '19: Proceedings of the 3rd International Conference on Graphics and Signal Processing

Pages 105 - 112

https://doi.org/10.1145/3338472.3338485

Published: 01 June 2019 Publication History

Abstract

Atrial fibrillation is a kind of common chronic arrhythmia. The incidence of atrial fibrillation increases with aging. Therefore, especially for the elderly, accurate detection of atrial fibrillation can effectively prevent stroke. In this paper, we propose a strategy that combines the heartbeat model based on deep learning with statistical heart rate features, using a classifier such as a multi-layer perceptron to identify atrial fibrillation rhythm. It is worth noticing that the heartbeat model that we used to extract features for the classification of heartbeat. Through this transfer learning method, the features of each heartbeat in the heart rhythm are extracted one by one for the identification task of atrial fibrillation. We evaluated the proposed method on the MIT-BIH AF dataset. The experimental result shows that under the attention mechanism, the accuracy of the proposed method is 98.91%, the sensitivity is 99.41% and the specificity is 98.50%, which outperforms most of the current algorithms.

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Cited By

Vu TPhuong DHuy HKien NHuong P(2024)A Sleep Apnea Detection Methodology Based on SE-ResNeXt Model Using Single-Lead ECGJournal of Biomimetics, Biomaterials and Biomedical Engineering10.4028/p-Cbr55F64(85-93)Online publication date: 10-Apr-2024
https://doi.org/10.4028/p-Cbr55F
WANG YLI YCHEN MHUO RLIU LLIANG YWEI S(2023)RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINEJournal of Mechanics in Medicine and Biology10.1142/S021951942350091424:04Online publication date: 4-Oct-2023
https://doi.org/10.1142/S0219519423500914
Li YChen MWang YLiang YWei S(2023)Diagnosis of atrial fibrillation based on lightweight detail-semantic networkBiomedical Signal Processing and Control10.1016/j.bspc.2023.10502585(105025)Online publication date: Aug-2023
https://doi.org/10.1016/j.bspc.2023.105025

Index Terms

Atrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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ICGSP '19: Proceedings of the 3rd International Conference on Graphics and Signal Processing

June 2019

127 pages

ISBN:9781450371469

DOI:10.1145/3338472

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The Hong Kong Polytechnic: The Hong Kong Polytechnic University
City University of Hong Kong: City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2019

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ICGSP '19: 2019 The 3rd International Conference on Graphics and Signal Processing

June 1 - 3, 2019

Hong Kong, Hong Kong

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Cited By

Vu TPhuong DHuy HKien NHuong P(2024)A Sleep Apnea Detection Methodology Based on SE-ResNeXt Model Using Single-Lead ECGJournal of Biomimetics, Biomaterials and Biomedical Engineering10.4028/p-Cbr55F64(85-93)Online publication date: 10-Apr-2024
https://doi.org/10.4028/p-Cbr55F
WANG YLI YCHEN MHUO RLIU LLIANG YWEI S(2023)RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINEJournal of Mechanics in Medicine and Biology10.1142/S021951942350091424:04Online publication date: 4-Oct-2023
https://doi.org/10.1142/S0219519423500914
Li YChen MWang YLiang YWei S(2023)Diagnosis of atrial fibrillation based on lightweight detail-semantic networkBiomedical Signal Processing and Control10.1016/j.bspc.2023.10502585(105025)Online publication date: Aug-2023
https://doi.org/10.1016/j.bspc.2023.105025

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