Predictability analysis of the heart rate variability

Sixteen acute myocardial infarction (AMI) inpatients were selected randomly. Sixteen normal subjects were selected as a control group, their age and sex matched to the AMI group. The patient´s HRV were recorded 9 times after AMI in half a year. Some predictability measures based on neural network learning were used to analyze these data. It was found that for normal subjects, their HRVs were chaotic, but for AMI patients, their HRVs could be either periodic or stochastic. For all the sixteen AMI patients, some of the predictability measures kept one order lower than the one for normal subjects at least for six months after AMI. Therefore, it can be a sensitive index for measuring the damage of the heart due to the AMI attack