ABSTRACT
In this study, we applied generalized autoregressive conditional heteroskedasticity (GARCH) model to conditional fluctuation characteristics of heart rate variation (HRV) series (congestive heart failure, Normal), with all the data from PhysioNet ECG database. Research results proved the existence of condition fluctuation characteristic in the series of changing rate of HRV. In the GARCH model family, threshold GARCH (1,1)(TGARCH (1,1)) model performs best in fitting changing rate of HRV. Although the structure of ARCH (1) model is simple, its error is the closest to that of TGARCH (1, 1) model. The results also showed that the difference was obvious between disease group and normal group. All these results provide a new method to the research and clinical application of HRV.
Subject(s)
Humans , Cardiology , Heart Failure , Heart Rate , Models, CardiovascularABSTRACT
Using the algorithm proposed by Costa M, et al., we studied the multiscale entropy (MSE) of electrocardiogram. The sample entropy (SampEn) of the healthy subjects was found to be higher than that of the subjects with coronary heart disease or myocardial infarction. The healthy subjects' complexity was found to be the highest. The SampEn of the subjects with coronary heart disease was noted to be only slightly higher than that of the subjects with myocardial infarction. These findings show that the complexity of the subjects with coronary heart disease or myocardial infarction is distinctly lower than the complexity of the healthy ones, and the subjects suffereing from coronary heart disease are liable to the onset of myocardial infarction.