Detection of Premature Ventricular Contractions using 12-lead Dynamic ECG based on Squeeze-Excitation Residual Network

Premature ventricular contraction (PVC) is a very common arrhythmia that can originate in any part of the ventricle and is one of the important causes of sudden cardiac death. Timely and rapid detection of PVC on dynamic electrocardiogram (ECG) recording for patients with cardiovascular diseases is of great significance for clinical diagnosis. Furthermore, it can facilitate the planning and execution of radiofrequency ablation. But the dynamic ECGs can be easily contaminated by various noises and its morphological characteristics show significant variations for different patients. Though the deep learning methods achieved outstanding performance in ECG automatic recognition, there are still some limitations, such as overfitting, gradient disappearance or gradient explosion in deep networks. Therefore, a residual module is constructed using the squeeze-excitation method to alleviate the problems. A 20-layer squeeze-extraction residual network (SE-ResNet) containing multiple squeeze-extraction modules was designed for real-time PVC detection on 12-lead dynamic ECG. The algorithm was evaluated using the dynamic 12-lead ECGs in INCART database (168,379 heartbeats in total). The experimental results show that the test accuracy of the method proposed in this paper is 98.71%, and the specificity and sensitivity of PVC are 99.12% and 99.59%, respectively. Under the same dataset and experimental platform, the average recognition accuracy of our proposed method is increased by 0.73%, 1.55%, 2.9% and 1.65% compared with the results obtained by CNN, Inception, AlexNet and deep multilayer perceptron, respectively. The proposed scheme provides a new method for real-time detection of PVC on dynamic 12-lead ECGs. The experiment results show that the proposed method outperforms state-of-the-art methods, and has good potential for clinical applications.

@article{Li2022,
  title     = {Detection of Premature Ventricular Contractions using 12-lead Dynamic ECG based on Squeeze-Excitation Residual Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Duan Li and Tingting Sun and Yibai Xue and Yilin Xie and Xiaolei Chen and Jiaofen Nan},
  doi       = {10.14569/IJACSA.2022.0130702},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130702}
}