CLSTM-Transformer: inter-patient ECG classification for ventricular arrhythmia

Jingxuan Wang; Tong Liu; Mujun Zang; Qingjun Wang

doi:10.1117/12.2678623

14 April 2023 CLSTM-Transformer: inter-patient ECG classification for ventricular arrhythmia

Jingxuan Wang, Tong Liu, Mujun Zang, Qingjun Wang

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340H (2023) https://doi.org/10.1117/12.2678623
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

Ventricular arrhythmia is a common arrhythmia diseases, which poses a severe threaten to human health. Electrocardiogram (ECG) carries plenty of pathological information about the heart activities of a patient and has been widely used for diagnosing arrhythmia. Despite some models that have been proposed so far for the automatic classification of arrhythmia by the features of the ECG, their performance will be limited, because the extracted features are relatively monotonous and simple. In order to improve the performance of arrhythmia classification, a Convolutional Neural Networks (CNN), Transformer and Long Short-Term Memory (LSTM) based assemble neural network framework named CLSTM-Transformer is proposed for automatic heartbeat classification under the inter-patient paradigm. It's worth noting that we use the transformer, which is rarely used in medical signaling to pay attention to the important heartbeats. Compared to most current single network models, CLSTM-Transformer extracts feature from three different levels, which can extract more hidden information in the heartbeats and makes the model more advantageous. The experiment results show that our model achieves a 98.56% accuracy, a 93.45% sensitivity, a 93.45% specificity, and a 98.54% positive predictive value, respectively. Compared with other models, this model has better classification performance, which makes it more applicable to the diagnosis of arrhythmia diseases.

Citation Download Citation

Jingxuan Wang, Tong Liu, Mujun Zang, and Qingjun Wang "CLSTM-Transformer: inter-patient ECG classification for ventricular arrhythmia", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340H (14 April 2023); https://doi.org/10.1117/12.2678623

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