| Autor: |
Hadiyoso, Sugondo, Aulia, Suci, Irawati, Indrarini Dyah, Ramdhani, Mohamad |
| Předmět: |
|
| Zdroj: |
International Journal of Online & Biomedical Engineering; 2022, Vol. 18 Issue 7, p151-160, 10p |
| Abstrakt: |
Automatic detection of heartbeat is critical for early cardiovascular disease prevention and diagnosis. Traditional feature methodologies based on expert knowledge cannot abstract and represent multidimensional and multi-view information. Hence traditional research on heartbeat detection pattern recognition cannot produce adequate results. The proposed method in this research used Dispersion Entropy (DisEn) on Multidistance Signal Level Difference (MSLD) for feature extraction and Support Vector Machine (SVM) method for classifying the ECG signals. The ECG datasets used in this research were obtained from the MIT-BIH Arrhythmia database. The experiments result using 5-fold cross-validation revealed that at distance D= 1-15 had the highest accuracy of 91% to classify the ECG data into Normal Sinus Rhythm (NSR), Left Bundle Branch Block (LBBB), and Atrial Fibrillation (AFIB) from the MIT-BIH Arrhythmias database. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
