| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系 === 103 === Electrocardiogram (ECG) signal contains important information that can help doctors diagnose. If it is normal or failure of the heart, ECG has some characteristics, irregular heartbeats, called arrhythmias. Generally arrhythmic people are by accident a few arrhythmic heartbeats who do not have much of an impact. However, if the frequency of arrhythmias increases, it is a warning, patients with increased risk of shock or sudden death will. The aim of this study was to investigate the use of heart rate variability (HRV) for the quantitative analysis of irregular arrhythmias incidence, set the frequency of 25% and 10%, when more than setting, as a heart is at high risk, as abnormal condition. This study used United States Massachusetts Institute of technology offers an arrhythmia database as a data source. In order to assess the identifying performance of the characteristics of HRV, we used 2 decision trees and support vector machine (SVM) classifier respectively which input features were time and frequency domain parameters of heart rate variability to identify the normal or abnormal condition. The classifiers by 10-fold cross-validation got the optimum parameters of HRV. The results show that support vector machine has the best accuracy of 95.3% when irregular arrhythmia rate is set at 25%. However, when irregular arrhythmia rate is set at 10%, the results show that support vector machine has the best accuracy was 92.0%.The present study showed that, we can evaluate the degree of risk of cardiac function by the HRV. This method is beneficial for those who know their patients with arrhythmias, to assess their heart function risk will increase.
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