Predicting coronary disease risk based on short-term RR interval measurements: a neural network approach.

Abstract

Coronary heart disease is a multifactorial disease and it remains the most common cause of death in many countries. Heart rate variability has been used for non-invasive measurement of parasympathetic activity and prediction of cardiac death. Patterns of heart rate variability associated with respiratory sinus arrhythmia have recently been considered as possible indicators of coronary heart disease risk in asymptomatic subjects. The aim of this work is to detect individuals at varying risk of coronary heart disease based on short-term heart rate variability measurements under controlled respiration. Artificial neural networks are used to recognise Poincaré-plot-encoded heart rate variability patterns related to coronary heart disease risk. The results indicate a relatively coarse binary representation of Poincaré plots could be superior to an analogue encoding which, in principle, carries more information.