  Ischemic heart disease (IHD), particularly in its chronic stable form, is a
subtle pathology due to its silent behavior before developing in unstable
angina, myocardial infarction or sudden cardiac death. Machine learning
techniques applied to parameters extracted form heart rate variability (HRV)
signal seem to be a valuable support in the early diagnosis of some cardiac
diseases. However, so far, IHD patients were identified using Artificial Neural
Networks (ANNs) applied to a limited number of HRV parameters and only to very
few subjects. In this study, we used several linear and non-linear HRV
parameters applied to ANNs, in order to confirm these results on a large cohort
of 965 sample of subjects and to identify which features could discriminate IHD
patients with high accuracy. By using principal component analysis and stepwise
regression, we reduced the original 17 parameters to five, used as inputs, for
a series of ANNs. The highest accuracy of 82% was achieved using meanRR, LFn,
SD1, gender and age parameters and two hidden neurons.
