A neural network approach to coronary heart disease risk assessment based on short-term measurement of RR intervals

Author

Azuaje, F. ; Dubitzky, W. ; Wu, X. ; Lopes, P. ; Black, N. ; Adamson, K ; White, JA

Author_Institution

NIBEC, Ulster Univ., UK

fYear

1997

fDate

7-10 Sep 1997

Firstpage

53

Lastpage

56

Abstract

Using short-term heart rate variability (HRV) measurements, this study investigates the relationship between respiratory sinus arrhythmia (RSA) and Coronary Heart Disease (CHD) risk in asymptomatic patients who nevertheless exhibit CHD risk factors. The aim is to train an artificial neutral network (ANN) to recognise HRV patterns related to CHD risk via a Poincare plot encoding. The ANN correctly classified 6 out of 9 `high´ 6 out of 9 `medium´, and 6 out of 9 `low´ risk test cases. It is expected that this result can be improved by increasing the number of input neurons and by using different preprocessing techniques. This study showed that an ANN approach can be successful in detecting individuals at varying risk of CHD based on short-term HRV measurements under controlled breathing

Keywords

electrocardiography; encoding; medical signal processing; neural nets; Poincare plot encoding; RR intervals; artificial neutral network training; asymptomatic patients; controlled breathing; coronary heart disease risk assessment; input neurons number; neural network approach; preprocessing techniques; respiratory sinus arrhythmia; short-term measurement; Artificial neural networks; Cardiac disease; Data preprocessing; Encoding; Heart rate variability; Neural networks; Neurons; Pattern recognition; Risk management; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Cardiology 1997

Conference_Location

Lund

ISSN

0276-6547

Print_ISBN

0-7803-4445-6

Type

conf

DOI

10.1109/CIC.1997.647828

Filename

647828