Principal component analysis of heart rate variability data in assessing cardiac autonomic neuropathy

Mika P. Tarvainen, David J. Cornforth, Herbert F. Jelinek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Heart rate variability (HRV) is recognized to carry early diagnostic value regarding cardiac autonomic neuropathy (CAN). A number of different HRV analysis algorithms have been proposed for the assessment of CAN, each of them providing partly differing information about HRV time series. Instead of confining to a limited set of HRV features, a multi-dimensional approach incorporating a multitude of HRV parameters could be an optimal way of assessing the changes in HRV related to CAN. In this paper, principal component analysis (PCA) is used for analysing multi-dimensional HRV data of 11 patients with definite CAN and 71 subjects without CAN. Using the two most significant principal components, patients with CAN were separated from subjects without CAN with 87% accuracy.

Original languageBritish English
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6667-6670
Number of pages4
ISBN (Electronic)9781424479290
DOIs
StatePublished - 2 Nov 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: 26 Aug 201430 Aug 2014

Publication series

Name2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Conference

Conference2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/TerritoryUnited States
CityChicago
Period26/08/1430/08/14

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