Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions

Siyuan Li; Xuesong Su; Qingyu Yao; Gongwen Chen

doi:10.1117/12.3004572

16 August 2023 Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions

Siyuan Li, Xuesong Su, Qingyu Yao, Gongwen Chen

Author Affiliations +

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 1278728 (2023) https://doi.org/10.1117/12.3004572
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. The model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. The experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. This innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyuan Li, Xuesong Su, Qingyu Yao, and Gongwen Chen "Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 1278728 (16 August 2023); https://doi.org/10.1117/12.3004572

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KEYWORDS

Convolution

Electrocardiography

Data modeling

Performance modeling

Cardiovascular disorders

Education and training

Signal processing

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