基于变尺度融合网络模型的心电数据识别算法  

Electrocardiogram data recognition algorithm based on variable scale fusion network model

作　　者：刘子龙[1] 陈鹏[1] LIU Zilong;CHEN Peng(School of Optoelectronic Information and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,P.R.China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《生物医学工程学杂志》2022年第3期570-578,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(61603255)。

摘　　要：心律失常类型的判断是早期心血管疾病预防和诊断的关键,因此心电图(ECG)分析作为医生诊断的重要依据得到了广泛应用。由于受到不同患者间ECG信号形态差异大、类别分布不平衡等因素影响,现有的心律失常自动检测算法在识别过程中存在一定的困难。本文提出了一种变尺度融合网络模型用于心律类型的自动识别,利用改进后的ECG生成网络(EGAN)模块解决了ECG数据不平衡问题,并以灰度递归图(GRP)和频谱图形式对ECG信号进行二维重现,结合模型的分支结构,实现了变长心拍的自动分类。研究结果采用麻省理工学院与贝斯以色列医院(MIT-BIH)心律失常数据库进行验证,对其中八种心律类型进行区分,平均准确率达到了99.36%,敏感性和特异性分别为96.11%、99.84%,未来期望本方法可用于临床辅助诊断以及智能穿戴设备等。The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram(ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network(EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot(GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital(MIT-BIH)arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.

关 键 词：心律失常 变尺度融合网络 心电生成网络 心电数据不平衡 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]