Novel Classification of Ischemic Heart Disease Using Artificial Neural Network

TL;DR

This study enhances early diagnosis of ischemic heart disease by applying artificial neural networks to a large set of heart rate variability parameters, achieving high accuracy with optimized feature selection.

Contribution

It introduces a comprehensive approach using multiple HRV parameters and feature reduction techniques to improve IHD classification accuracy with ANNs on a large cohort.

Findings

Achieved 82% classification accuracy for IHD.

Reduced 17 HRV parameters to 5 key features.

Validated effectiveness on a large sample of 965 subjects.

Abstract

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.