COPD severity classification using principal component and cluster analysis on HRV parameters

D. A. Newandee, S. S. Reisman, M. N. Bartels, R. E. De Meersman

Research output: Contribution to journalConference articlepeer-review

12 Scopus citations

Abstract

The application of the principal component analysis and cluster analysis (PCACA) using Heart Rate Variability (HRV) parameters to identify the most severe Chronic Obstructive Pulmonary Disease (COPD) subjects in a mixture of normal and COPD population is discussed. These parameters were obtained from real physiological data and cross-spectral analysis (i.e. the coherence and partial coherence between heart rate, blood pressure and respiration signals). Results demonstrated that these two groups could be differentiated with greater than 99.0% accuracy. Furthermore, differences on the same HRV parameters between all four severity levels of COPD subjects were also investigated. These groups were differentiated with over 88.0% accuracy. In analyzing the studied data of the COPD population, the technique correctly characterized 8.5% of COPD group as severe COPD. It was concluded that the PCA-CA technique identified the combination of parameters that can classify disease severity (COPD) as well as differences between normal and COPD subjects in a mixed population. The PCA-CA technique could perhaps also be used to classify other diseases non-invasively.

Original languageEnglish (US)
Pages (from-to)134-135
Number of pages2
JournalProceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
StatePublished - 2003
Externally publishedYes
EventProceedings of the IEEE 29th Annual Northeast Bioengineering Conference - Newark, NJ, United States
Duration: Mar 22 2003Mar 23 2003

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Bioengineering

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