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

2020/10/29 by Giulia Silveri, Silveri, Giulia, Marco Merlo +13
Medicine · Engineering · #Heart Rate Variability and Autonomic Control #ECG Monitoring and Analysis #Non-Invasive Vital Sign Monitoring

paper · pdf · doi:10.48550/arxiv.2010.15893

Abstract

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

Related

• Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review
• Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity
• Machine Learning-Based Automatic Cardiovascular Disease Diagnosis Using Two ECG Leads
• A Novel Approach to the Diagnosis of Heart Disease using Machine\n Learning and Deep Neural Networks
• A Machine Learning model of the combination of normalized SD1 and SD2\n indexes from 24h-Heart Rate Variability as a predictor of myocardial\n infarction