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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (4): 581-589.DOI: 10.11996/JG.j.2095-302X.2021040581

• 图像处理与计算机视觉 • 上一篇    下一篇

基于多尺度特征提取的多导联心跳信号分类

  

  1. 1. 浙江工业大学计算机科学与技术学院，浙江 杭州 310023； 2. 吉林大学软件学院，吉林 长春 130000
  • 出版日期:2021-08-31 发布日期:2021-08-05
  • 基金资助:
    国家自然科学基金项目(61801428)

Multi-lead heartbeat signal classification based on multi-scale feature extraction

  1. 1. School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou Zhejiang 310023, China;
    2. College of Software, Jilin University, Changchun Jilin 130000, China
  • Online:2021-08-31 Published:2021-08-05
  • Supported by:
    National Natural Science Foundation of China (61801428)

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摘要： 心电图(ECG)是临床上诊断心脏疾病的重要依据，从中提取关键、有效的特征是自动诊断系统
的关键。而现今多数研究仅使用单导联或双导联数据，提取的特征不够全面，无法很好地区分不同心跳中的细
微差别。为了获得更加全面的特征和更优异的分类表现，本文提出了基于多尺度特征提取的多导联心跳信号分
类方法(MSNet)。首先，该方法接收多导联心跳信号堆叠矩阵作为输入；然后，利用 3 种不同尺度的一维卷积
分别提取特征；最后将不同尺度的特征融合并进行所属类别的分类。本文在 MIT-BIH Arrhythmia Database，
MIT-BIHSupraventricular Arrhythmia Database和St Petersburg INCART 12-lead Arrhythmia Database 3个心电公开
数据集上进行了充分的实验，在五折交叉验证的策略下，对于“正常-异常”分类，该方法的准确率、召回率、
精确率、F1 值均达到了 99%以上；对于多类别分类，其平均准确率、平均召回率、平均精确率、平均 F1 值能
达到 99.5%左右。与现今优异的其他方法相比，该方法有着更好的表现。

关键词: 心跳分类, 多尺度特征提取, 特征融合

Abstract: Electrocardiogram (ECG) is important for clinical diagnosis of cardiovascular disease. Extracting effective
features is the key to automatic diagnosis system. Nowadays, most research works only use single-lead or two-lead
signals, which makes the extracted features too incomprehensive to achieve excellent classification performance. In
order to obtain more comprehensive features and better classification results, we proposed a multi-lead heartbeat
signal classification method based on multi-scale feature extraction (MSNet). Firstly, the method received multi-lead
stacked heartbeats signal matrix as input. Then, three feature vectors with different scale information were extracted
by convolution layers. Finally, classification was designed to decide to which category the input heartbeat belonged
based on fused features. Three open source databases including MIT-BIH Arrhythmia Database, MIT-BIH
Supraventricular Arrhythmia Database, and St Petersburg INCART 12-Lead Arrhythmia Database were utilized in this research. Experiments on these databases with 5-fold cross-validation strategy show the excellent performance of the
proposed method. For “normal-abnormal” classification, more than 99% of the accuracy, recall, precision, and F1
score can be achieved. For “multi-category” classification, 99.5% of the average accuracy, average recall, average
precision, and average F1 score can be achieved. Compared with the state-of-the-art methods, the proposed method
exhibits better performance.

Key words: heartbeat classification, multi-scale feature extraction, feature fusion

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