Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (3): 30-36.doi: 10.6040/j.issn.1672-3961.0.2020.445

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Automatic detection research of arrhythmia based on CNN-LSTM hybrid model

TAO Liang1, LIU Baoning1, LIANG Wei2*   

  1. 1. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China;
    2. School of Electrical Engineering and Automation, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, Shandong, China
  • Online:2021-06-20 Published:2021-06-24

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Abstract: A hybrid algorithm of convolutional neural network and long short-term memory network was proposed for automatic detection of arrhythmias. The model structure was composed of 5 convolutional layers, 5 pooled layers, 1 LSTM layer and 1 fully connected layer. By taking advantage of CNN's ability to automatically extract features and LSTM's ability to capture dependencies before and after time series, the simple preprocessed ECG signal data were directly input into the hybrid model. The whole model combined the two steps of feature extraction and classifier classification, so as to identify five different arrhythmias more efficiently and accurately. The accuracy, sensitivity and specificity of the test set were 99.48%, 99.47% and 99.86% respectively. The experimental results showed that the proposed method could efficiently and accurately identify different types of arrhythmias.

Key words: CNN, LSTM, automatic detection, electrocardiogram, arrhythmia

CLC Number: 

  • TP391
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