Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine

作者：Yang, Weiyi; Si, Yujuan^*; Wang, Di; Guo, Buhao

来源：Computers in Biology and Medicine, 2018, 101: 22-32.

DOI：10.1016/j.compbiomed.2018.08.003

• Principal component analysis network
• Arrhythmia recognition
• Noise robustness
• Deep learning
• Cardiovascular diseases

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of this study is to apply a principal component analysis network (PCANet) for feature extraction based on a noisy ECG signal. To improve the classification speed, a linear support vector machine (SVM) was applied. In our experiments, we identified five types of imbalanced original and noise-free ECGs in the MIT-BIH arrhythmia database to verify the effectiveness of our algorithm and achieved 97.77% and 97.08% accuracy, respectively. The results show that our method has high recognition accuracy in the classification of skewed and noisy - heartbeats, indicating that our method is a practical ECG recognition method with suitable noise robustness and skewed data applicability.

• 出版日期2018-10-1
• 单位吉林大学