These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.
    Author: Yang W, Si Y, Wang D, Guo B.
    Journal: Comput Biol Med; 2018 Oct 01; 101():22-32. PubMed ID: 30098452.
    Abstract:
    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.
    [Abstract] [Full Text] [Related] [New Search]