%A TAO Liang, LIU Baoning, LIANG Wei %T Automatic detection research of arrhythmia based on CNN-LSTM hybrid model %0 Journal Article %D 2021 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2020.445 %P 30-36 %V 51 %N 3 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_2035.shtml} %8 2021-06-20 %X 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.