In the modern clinical diagnosis, the 12-lead electrocardiogram (ECG) signal has proved effective in cardiac arrhythmias classification. However, the manual diagnosis for cardiac arrhythmias is tedious and error-prone through ECG signals. In this work, we propose an end-to-end deep neural network called attention-based Res-BiLSTM-Net for automatic diagnosis of cardiac arrhythmias. Our model is capable of classifying ECG signals with different lengths. The proposed network consists of two parts: the attention-based Resnet and the attention-based BiLSTM. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by our attention-based Resnet through signal segments. Next, these multi-scale features from a same ECG signal are integrated in chronological order. In the end, our attention-based BiLSTM classifies cardiac arrhythmias according to combined features. Our method achieved a good result with an average F1score of 0.8757 on a multi-label arrhythmias classification problem in the First China ECG Intelligent Competition.


Cardiac arrhythmias classification Attention Resnet BiLSTM 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 61672231), Shanghai Natural Science Foundation (Grant No. 18ZR1411400), and Fundamental Research Funds for the Central Universities.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chengbin Huang
    • 1
  • Renjie Zhao
    • 1
  • Weiting Chen
    • 1
    Email author
  • Huazheng Li
    • 1
  1. 1.MOE Engineering Research Center for Hardware/Software Co-Design Technology and Application, Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityShanghaiChina

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