Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability

TL;DR

This study demonstrates that machine learning algorithms, specifically artificial neural networks, can accurately diagnose ischemic heart disease using non-invasive heart rate variability parameters from ECG data, offering a promising diagnostic tool.

Contribution

The paper introduces a non-invasive method for IHD diagnosis using HRV features and neural networks, achieving high accuracy with a simplified parameter set.

Findings

Achieved 98.9% training accuracy

Achieved 82% validation accuracy

Used 18 features including HRV and demographic data

Abstract

The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from HRV, a signal easily obtained from the ECG. In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects (156 normal subjects and 87 IHD patients) were used to train and validate a series of several ANN, different for number of input and hidden nodes. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset, respectively.