摘要
心血管疾病是我国死亡率较高的疾病之一,通过观察心电图来判断心电信号是否出现异常能够对心血管疾病进行预防和筛查。由于心电图数据规模大且繁杂,临床医护人员在心电图筛查时,工作负担大且容易出现误诊或漏诊的情况。为了提高心电图的筛查效率、减少医护人员的压力,提出了一种基于卷积神经网络、长短期记忆神经网络和SE网络的心电图分类算法模型(CNN-LSTM-SE),该模型将心电图分成5种不同的类别。主要研究内容包括:选用MIT-BIH心律失常数据集作为心电信号的数据来源,使用巴特沃斯带通滤波器对心电信号进行去噪处理,通过Z-score方法对心电信号进行标准化处理,利用独热编码方法对心电信号标签进行编码,最后使用处理后的心电数据对所提算法模型进行训练和测试。实验结果表明:所提模型相较于其它模型,能够有效提高心电图分类的准确性,在实验数据集上的分类准确率达到99.1%。
Cardiovascular disease is one of the diseases with high mortality rate in China.Monitoring electrocardiograms to determine if there are abnormalities in the electrical signals of the heart can be used to prevent and screen for cardiovascular disease.Due to the large scale and complexity of electrocardiogram data,clinical medical staff have a heavy workload and are prone to misdiagnosis or missed diagnosis during electrocardiogram screening.In order to improve the screening efficiency of electrocardiogram and reduce the pressure on medical staff,a model based on convolutional neural network,long and short-term memory neural network and SE network(CNN-LSTM-SE)was proposed to divide electrocardiogram into five categories.The main research contents include:MIT-BIH arrhythmia data set is selected as the data source of ECG signals,Butterworth bandpass filter is used to de-noise ECG signals,Z-score method is used to standardize ECG signals,and unique thermal coding method is used to encode ECG labels.Finally,the proposed algorithm model is trained and tested using the processed ECG data.The experimental results show that compared with other models,the proposed model can effectively improve the accuracy of ECG classification,and the classification accuracy of the experimental data set reaches 99.1%.
作者
王建荣
邓黎明
程伟
李国翚
WANG Jianrong;DENG Liming;CHENG Wei;LI Guohui(College of Intelligence and Computing,Tianjin University,Tianjin 300000,China;School of Automation and Software,Shanxi University,Taiyuan 030000,China;Department of Product R&D,Tianjin Development Zone Orking High Tech.Co.,Ltd.,Tianjin 300000,China)
出处
《测试技术学报》
2024年第3期264-273,共10页
Journal of Test and Measurement Technology
基金
国家重点研发计划资助项目(2018YFC2000701)
中国博士后科学基金资助项目(2021M692400)
山西省基础研究计划资助项目(202203021221017)。
关键词
心律失常
心电图
卷积神经网络
SE网络
长短期记忆神经网络
arrhythmia
electrocardiogram
convolutional neural network(CNN)
SE net
long and short term memroy neural network(LSTM)
