电子测量与仪器学报

梁国祥,韩亮.使用多特征融合的心律失常分类方法[J].电子测量与仪器学报,2024,38(7):109-115

使用多特征融合的心律失常分类方法

Method on arrhythmia classification utilizing multi-feature fusion

DOI：

英文关键词:arrhythmia multi-feature fusion BCAR-NET short time Fourier transform wavelet transformation

基金项目:国家自然科学基金（62171066）项目资助

作者	单位
梁国祥	重庆大学微电子与通信工程学院重庆401331
韩亮	1.重庆大学微电子与通信工程学院重庆401331；2.生物感知与多模态智能信息处理重庆市重点实验室重庆401331

Author	Institution
Liang Guoxiang	School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
Han Liang	1.School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China; 2.Chongqing Key Lab of Bio-perception & Multimodal Intelligent Information Processing, Chongqing 401331, China

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中文摘要:

心律失常是一种常见的心血管疾病，它会严重影响患者的生活质量和生命安全。利用心电信号(ECG)进行心律失常自动分类对于其及时诊断与防治具有重要意义。为此，提出一种使用多特征融合的心律失常分类方法。首先从去噪后的心电信号中分别提取短时傅里叶(STFT)特征和小波(WT)特征。然后将STFT特征输入分支聚合残差网络(BCAR-NET)进行特征提取，获得其深度STFT特征；将WT特征输入1D-CNN网络，获得其深度WT特征；将原始ECG输入LSTM网络，获得其深度ECG特征。最后使用全连接网络将3种深度特征进行拼接和融合，进而实现心律失常分类。使用MIT-BIH心律失常数据库进行实验，所提出的使用多特征融合的心律失常分类方法的准确率为98.66%，F1分数的宏平均为94.22%，优于传统心律失常分类方法。实验结果表明，所构建的多特征融合网络有效利用了深度STFT特征、WT特征和ECG特征之间的互补性，提升了心律失常的分类性能。

英文摘要:

Arrhythmias is a common cardiovascular disease, which seriously affects the quality of life and safety of patients. The automatic classification of arrhythmia utilizing electrocardiogram (ECG) is of great significance for timely diagnosis and prevention. An arrhythmia classification method using multi-feature fusion is proposed. Firstly, the short time Fourier transform (STFT) features and wavelet transform (WT) features are respectively extracted from denoised ECG. Then, its deep STFT features is obtained by the branch aggregated residual network (BCAR-NET) with STFT features as input, and its deep WT features is obtained by the 1D-CNN with WT features as input. Moreover, the LSTM is used to extract deep ECG features. Finally, a fully connected network is used to concatenate and fuse the three deep features, then arrhythmia classification is realized. The proposed arrhythmia classification method is evaluated on the MIT-BIH arrhythmia dataset. The accuracy of the proposed method is 98.66%, and the macro-average F1 score is 94.22%, which is better than traditional arrhythmia classification methods. The experimental results show that the constructed multi-feature fusion network improves the classification performance of arrhythmia by effectively exploiting the complementarity between deep STFT features, WT features, and ECG features.

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