Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features

Applied Sciences

卷:11

Heechang Lee¹  Yebin Ji¹  Seongwoo Sim¹  Daesung Kang¹  Taeyoung Yoon¹  Chaeyun Yeo¹  HyeonYoung Oh¹ 

[1] Department of Healthcare Information Technology, Inje University, 197, Inje-ro, Gimhae-si 50834, Korea;

关键词: electrocardiogram (ECG);    1D feature extraction;    gray-level co-occurrence matrix (GLCM);    gray-level run-length matrix (GLRLM);   

DOI  :  10.3390/app11209460

来源: DOAJ

【 摘 要 】

The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.

【 授权许可】

Unknown