Binary classification for atrial fibrillation detection from ECG-based signals using 1D-convolutional neural network

Yuxi Chen; Huayiting Qiao; Alexander Wen

doi:10.1117/12.2626463

21 December 2021 Binary classification for atrial fibrillation detection from ECG-based signals using 1D-convolutional neural network

Yuxi Chen, Huayiting Qiao, Alexander Wen

Author Affiliations +

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215614 (2021) https://doi.org/10.1117/12.2626463
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Atrial fibrillation (AF) detection is essential for the timely prevention and intervention of strokes and pulmonary embolisms. The deep learning (DL)-based assistive detection system can effectively diagnose the AF condition by distinguishing the normal and AF signals from an electrocardiogram (ECG). This paper proposes and implements a simple 1D convolutional neural network (CNN) for binary classification – between normal and AF signals. The dataset used is obtained from the Computing in Cardiology 2017 challenge on AF detection, which is composed of short ECG recordings. The proposed method indicates that the model resulted in an F1 score of 0.767 with 72.9% testing accuracy. Feature extraction from model testing yields distinctness in features between normal and AF signals. The project results have successfully validated that the proposed 1D CNN method can achieve satisfactory and reasonable performance in classifying ECG signals.

Citation Download Citation

Yuxi Chen, Huayiting Qiao, and Alexander Wen "Binary classification for atrial fibrillation detection from ECG-based signals using 1D-convolutional neural network", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215614 (21 December 2021); https://doi.org/10.1117/12.2626463

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KEYWORDS

Atrial fibrillation

Signal detection

Electrocardiography

Data modeling

Feature extraction

Performance modeling

Principal component analysis

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