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A Multi-dilation Convolution Neural Network for Atrial Fibrillation Detection

Published: 10 September 2020 Publication History

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia whose management requires long-term automatic monitoring. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. In this article, we propose a multi-dilation CNN (convolution neural network) for Atrial fibrillation detection. Our proposed model is provided with the ability to extract multi-scale features with fewer parameters by designing MSDC (multi-scale dilation convolution) blocks. The evaluation is performed on the MIT-BIH Atrial Fibrillation Database, and our multi-dilation CNN composed of MSDC blocks outperforms other methods with a sensitivity of 99.45% and specificity of 99.61%. Compared to ResNet the state of art method before, our model performs better with only a quarter parameter size of Reset, which is useful for the implementation of edge computing-based deep learning network on wearable ECG devices.

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Cited By

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  • (2025)Research on Multi‐Scale Parallel Joint Optimization CNN for Arrhythmia DiagnosisConcurrency and Computation: Practice and Experience10.1002/cpe.838337:4-5Online publication date: 10-Feb-2025
  • (2024)IMC-ResNet: Atrial fibrillation detection method based on interlayer multiscale couplingBiomedical Signal Processing and Control10.1016/j.bspc.2024.10668397(106683)Online publication date: Nov-2024
  • (2023)D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detectionBiomedical Signal Processing and Control10.1016/j.bspc.2023.10461582(104615)Online publication date: Apr-2023
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  1. A Multi-dilation Convolution Neural Network for Atrial Fibrillation Detection

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    ICDSP '20: Proceedings of the 2020 4th International Conference on Digital Signal Processing
    June 2020
    383 pages
    ISBN:9781450376877
    DOI:10.1145/3408127
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 September 2020

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    Author Tags

    1. Atrial fibrillation
    2. ResNet
    3. dilation convolution
    4. wearable ECG devices

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    View all
    • (2025)Research on Multi‐Scale Parallel Joint Optimization CNN for Arrhythmia DiagnosisConcurrency and Computation: Practice and Experience10.1002/cpe.838337:4-5Online publication date: 10-Feb-2025
    • (2024)IMC-ResNet: Atrial fibrillation detection method based on interlayer multiscale couplingBiomedical Signal Processing and Control10.1016/j.bspc.2024.10668397(106683)Online publication date: Nov-2024
    • (2023)D2AFNet: A dual-domain attention cascade network for accurate and interpretable atrial fibrillation detectionBiomedical Signal Processing and Control10.1016/j.bspc.2023.10461582(104615)Online publication date: Apr-2023
    • (2021)Review of Deep Learning-Based Atrial Fibrillation Detection StudiesInternational Journal of Environmental Research and Public Health10.3390/ijerph18211130218:21(11302)Online publication date: 28-Oct-2021
    • (2021)Atriyal Fibrilasyon Tespiti için Evrişimli Sinir Ağı Tabanlı Bir Derin Ağ ModeliA Convolutional Neural Network Based Deep Network Model for Atrial Fibrillation DetectionDüzce Üniversitesi Bilim ve Teknoloji Dergisi10.29130/dubited.10112469:6(230-236)Online publication date: 31-Dec-2021
    • (2021)Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordingsComputers in Biology and Medicine10.1016/j.compbiomed.2021.104880139:COnline publication date: 1-Dec-2021

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