التفاصيل البيبلوغرافية
| العنوان: |
Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features |
| المؤلفون: |
Heechang Lee, Taeyoung Yoon, Chaeyun Yeo, HyeonYoung Oh, Yebin Ji, Seongwoo Sim, Daesung Kang |
| المصدر: |
Applied Sciences; Volume 11; Issue 20; Pages: 9460 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2021 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
electrocardiogram (ECG), 1D feature extraction, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM) |
| جغرافية الموضوع: |
agris |
| الوصف: |
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. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Computing and Artificial Intelligence; https://dx.doi.org/10.3390/app11209460 |
| DOI: |
10.3390/app11209460 |
| الاتاحة: |
https://doi.org/10.3390/app11209460 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.6535340F |
| قاعدة البيانات: |
BASE |