Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (12): 292-297.doi: 10.11896/jsjkx.190500181

Special Issue: Medical Imaging

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

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

FAN Min1, WANG Xiao-feng1, MENG Xiao-feng2   

  1. (Fenyang College of Shanxi Medical University,Fenyang,Shanxi 032200,China )1;
    (School of Information,Renmin University of China,Beijing 100872,China)2
  • Received:2019-05-31 Online:2019-12-15 Published:2019-12-17

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Abstract: 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.

Key words: Adaptive, ECG classification, LSTM, Wearable devices

CLC Number: 

  • TP181
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