基于可穿戴设备的心电图自适应分类算法研究  

Study on Patient-adaptive Algorithm for ECG Classification Based on Wearable Devices

作　　者：樊敏[1] 王晓锋[1] 孟小峰[2] FAN Min;WANG Xiao-feng;MENG Xiao-feng(Fenyang College of Shanxi Medical University,Fenyang,Shanxi 032200,China;School of Information,Renmin University of China,Beijing 100872,China)

机构地区：[1]山西医科大学汾阳学院,山西汾阳032200 [2]中国人民大学信息学院,北京100872

出　　处：《计算机科学》2019年第12期292-297,共6页Computer Science

摘　　要：目前,心血管疾病已成为全球人类非传染性死亡的主要原因,死亡人数约占全球死亡总人数的1/3,且患病人数逐年增加。可穿戴设备被用于对心电图进行自动分类,以实现对心血管疾病的早监测、早预防。随着边缘机器学习和联邦学习的兴起,小型机器学习模型成为了人们关注的热点。针对可穿戴心电图设备低配置、低功耗及个性化的特点,文中研究了一种基于LSTM的轻量级网络结构,并采用自适应算法来优化病人个体的心电图分类模型。该模型利用MIT-BIH公开数据集开展实验,将VEB和SVEB的分类效果与其他相关研究进行了比较。实验结果表明,所提算法的模型结构简单且分类识别率高,能够满足可穿戴设备对病人心电图监测的需求。At present,cardiovascular diseases have become the main cause of global non-communicable death,death toll accounts for about one third of the total toll of death in the world,and the number of patients is increasing year by year.Wearable devices is used to automaticaly classify electrocardiogram to facilitate the early monitoring and prevention of cardiovascular diseases for patients.With the rise of edge machine lear-ning and federated learning,small machine learning models have become a hot issue.According to the characteristics of wearable electrocardiogram equipment such as low configuration,low power consumption and personalization,this paper studied a lightweight network model based on LSTM,and used adaptive algorithm to optimize the ECG classification model of individual patients.The experiment is conducted by using the MIT-BIH open dataset.And compared with the current studies on the detection performance of VEB and SVEB,the experiment results show that the proposed algorithm has simple model structure and high classification performance,which can meet the requirement of ECG monitoring for patients by wearable devices.

关 键 词：可穿戴设备 心电图分类 自适应 LSTM 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]