Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy

David Cornforth, Mika Tarvainen, Herbert Jelinek

Research output: Book chapter/Published conference paperConference paper

1 Citation (Scopus)

Abstract

Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated co-morbidities. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). However, if possible we wish to detect CAN in its early stage, to improve treatment and outcomes. HRV provides information only on the interval between heart beats, but is relatively non-invasive and easy to obtain. HRV has been conventionally analysed with time- and frequency-domain methods, however more recent analysis methods have shown an increased sensitivity for identifying risk of future morbidity and mortality in diverse patient groups. A promising non-linear method is the Renyi entropy, which is calculated by considering the probability of sequences of values occurring in the HRV data. An exponent ÃŽ± of the probability can be varied to provide a spectrum of measures. In previous work we have shown a difference in the Renyi spectrum between participants identified with CAN and controls. In this work we applied the multi-scale Renyi entropy, as well as a variety of other measures, for identification of early CAN in diabetes patients, using computational intelligence methods. The work was based on measurements from 67 people with early CAN and 71 controls. Results suggest that Renyi entropy forms a useful contribution to the detection of CAN even in the early stages of the disease, and that it can be distinguished from controls with a correct rate of 68%. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.
Original languageEnglish
Title of host publicationICIEA 2013
Subtitle of host publication8th Proceedings
Place of PublicationUnited States
PublisherIEEE
Pages929-934
Number of pages6
ISBN (Electronic)9781467363211
DOIs
Publication statusPublished - 2013
EventIEEE Conference on Industrial Electronics and Applications - Melbourne, Australia, Australia
Duration: 19 Jun 201321 Jun 2013

Conference

ConferenceIEEE Conference on Industrial Electronics and Applications
CountryAustralia
Period19/06/1321/06/13

Fingerprint

Artificial Intelligence
Heart Rate
Entropy
Morbidity
Mortality

Cite this

Cornforth, D., Tarvainen, M., & Jelinek, H. (2013). Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy. In ICIEA 2013: 8th Proceedings (pp. 929-934). United States: IEEE. https://doi.org/10.1109/ICIEA.2013.6566500
Cornforth, David ; Tarvainen, Mika ; Jelinek, Herbert. / Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy. ICIEA 2013: 8th Proceedings. United States : IEEE, 2013. pp. 929-934
@inproceedings{10803327a3cd46f3838e7f0d158902e0,
title = "Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy",
abstract = "Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated co-morbidities. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). However, if possible we wish to detect CAN in its early stage, to improve treatment and outcomes. HRV provides information only on the interval between heart beats, but is relatively non-invasive and easy to obtain. HRV has been conventionally analysed with time- and frequency-domain methods, however more recent analysis methods have shown an increased sensitivity for identifying risk of future morbidity and mortality in diverse patient groups. A promising non-linear method is the Renyi entropy, which is calculated by considering the probability of sequences of values occurring in the HRV data. An exponent {\~A}Ž± of the probability can be varied to provide a spectrum of measures. In previous work we have shown a difference in the Renyi spectrum between participants identified with CAN and controls. In this work we applied the multi-scale Renyi entropy, as well as a variety of other measures, for identification of early CAN in diabetes patients, using computational intelligence methods. The work was based on measurements from 67 people with early CAN and 71 controls. Results suggest that Renyi entropy forms a useful contribution to the detection of CAN even in the early stages of the disease, and that it can be distinguished from controls with a correct rate of 68{\%}. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.",
author = "David Cornforth and Mika Tarvainen and Herbert Jelinek",
note = "Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2013. Event dates (773o) = 19-21 June, 2013; Parent title (773t) = IEEE Conference on Industrial Electronics and Applications.",
year = "2013",
doi = "10.1109/ICIEA.2013.6566500",
language = "English",
pages = "929--934",
booktitle = "ICIEA 2013",
publisher = "IEEE",

}

Cornforth, D, Tarvainen, M & Jelinek, H 2013, Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy. in ICIEA 2013: 8th Proceedings. IEEE, United States, pp. 929-934, IEEE Conference on Industrial Electronics and Applications, Australia, 19/06/13. https://doi.org/10.1109/ICIEA.2013.6566500

Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy. / Cornforth, David; Tarvainen, Mika; Jelinek, Herbert.

ICIEA 2013: 8th Proceedings. United States : IEEE, 2013. p. 929-934.

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - Computational intelligence methods for the identification of early Cardiac Autonomic Neuropathy

AU - Cornforth, David

AU - Tarvainen, Mika

AU - Jelinek, Herbert

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2013. Event dates (773o) = 19-21 June, 2013; Parent title (773t) = IEEE Conference on Industrial Electronics and Applications.

PY - 2013

Y1 - 2013

N2 - Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated co-morbidities. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). However, if possible we wish to detect CAN in its early stage, to improve treatment and outcomes. HRV provides information only on the interval between heart beats, but is relatively non-invasive and easy to obtain. HRV has been conventionally analysed with time- and frequency-domain methods, however more recent analysis methods have shown an increased sensitivity for identifying risk of future morbidity and mortality in diverse patient groups. A promising non-linear method is the Renyi entropy, which is calculated by considering the probability of sequences of values occurring in the HRV data. An exponent ÃŽ± of the probability can be varied to provide a spectrum of measures. In previous work we have shown a difference in the Renyi spectrum between participants identified with CAN and controls. In this work we applied the multi-scale Renyi entropy, as well as a variety of other measures, for identification of early CAN in diabetes patients, using computational intelligence methods. The work was based on measurements from 67 people with early CAN and 71 controls. Results suggest that Renyi entropy forms a useful contribution to the detection of CAN even in the early stages of the disease, and that it can be distinguished from controls with a correct rate of 68%. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.

AB - Cardiac Autonomic Neuropathy (CAN) is a disease that involves nerve damage leading to abnormal control of heart rate. CAN affects the correct operation of the heart and in turn leads to associated co-morbidities. An open question is to what extent this condition is detectable by the measurement of Heart Rate Variability (HRV). However, if possible we wish to detect CAN in its early stage, to improve treatment and outcomes. HRV provides information only on the interval between heart beats, but is relatively non-invasive and easy to obtain. HRV has been conventionally analysed with time- and frequency-domain methods, however more recent analysis methods have shown an increased sensitivity for identifying risk of future morbidity and mortality in diverse patient groups. A promising non-linear method is the Renyi entropy, which is calculated by considering the probability of sequences of values occurring in the HRV data. An exponent ÃŽ± of the probability can be varied to provide a spectrum of measures. In previous work we have shown a difference in the Renyi spectrum between participants identified with CAN and controls. In this work we applied the multi-scale Renyi entropy, as well as a variety of other measures, for identification of early CAN in diabetes patients, using computational intelligence methods. The work was based on measurements from 67 people with early CAN and 71 controls. Results suggest that Renyi entropy forms a useful contribution to the detection of CAN even in the early stages of the disease, and that it can be distinguished from controls with a correct rate of 68%. This is a significant achievement given the simple nature of the information collected, and raises prospects of a simple screening test and improved outcomes of patients.

U2 - 10.1109/ICIEA.2013.6566500

DO - 10.1109/ICIEA.2013.6566500

M3 - Conference paper

SP - 929

EP - 934

BT - ICIEA 2013

PB - IEEE

CY - United States

ER -