基于Inception模块的CNN-BiLSTM房颤检测与心拍分类算法

doi:10.3969/j.issn.0258-8021.2024.04.007

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Abstract Automatic ECG classification technology is an important auxiliary diagnostic method for arrhythmia. In order to improve the accuracy of abnormal dynamic ECG heartbeat extraction, an ECG beat classification algorithm based on Inception module and CNN-BiLSTM was proposed in this paper. First, the ECG signal was divided into heartbeat segments with the length of 1000 sampling points. Next, 3 different scales heartbeats were extracted by using the Inception module. The ECG features were further extracted through a 4-layer one-dimensional convolutional neural network (CNN) and a 2-layer bidirectional long short-term memory neural network (BiLSTM). At last, a 1-layer fully connected network and a softmax function were used to reduce the dimension of feature and classify the heartbeat. To improve the classification accuracy, a wavelet denoising technique was used to reduce the noise of the raw data. The data provided by the PhysioNet/Computing in Cardiology Challenge 2017 database were used in experiments. After preprocessed, 60,000 heartbeat samples were selected for classification, and the accuracy (Acc) and F1 score (F1-score) were used as the main evaluation criteria to evaluate the performance of the model. Results showed that the established model had an Acc of 91.38 % for the three types of heartbeats (normal, atrial fibrillation, and others) and F1-score was 91.27%, which was 4.77% and 4.59% higher than that of the combined model using only CNN-BiLSTM (Acc of 86.61%, F1-score of 86.68%), respectively. In conclusion, the proposed CNN-BiLSTM atrial fibrillation detection and ECG beat classification algorithm based on the Inception module has a better classification efficacy than the CNN-BiLSTM combined model.

Key words： arrhythmia ECG heartbeat classification convolutional neural network long short-term memory neural network

Received: 15 July 2022

PACS:

R318

Corresponding Authors: ^*E-mail: yjl@jiangnan.edu.cn

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	Zhang Yao
	Liu Yanjun
	Liu Lei

Cite this article:

Zhang Yao,Liu Yanjun,Liu Lei. Atrial Fibrillation Detection and ECG Heartbeat Classification Algorithm Based on Inception Module and CNN-BiLSTM[J]. Chinese Journal of Biomedical Engineering, 2024, 43(4): 447-454.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2024.04.007 OR http://cjbme.csbme.org/EN/Y2024/V43/I4/447

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