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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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)
Title of host publicationBioengineering, Proceedings of the Northeast Conference
EditorsS. Reisman, R. Foulds, B. Mantilla
Pages134-135
Number of pages2
StatePublished - 2003
Externally publishedYes
EventProceedings of the IEEE 29th Annual Northeast Bioengineering Conference - Newark, NJ, United States
Duration: Mar 22 2003Mar 23 2003

Other

OtherProceedings of the IEEE 29th Annual Northeast Bioengineering Conference
CountryUnited States
CityNewark, NJ
Period3/22/033/23/03

Fingerprint

Pulmonary diseases
Cluster analysis
Principal component analysis
Blood pressure
Spectrum analysis

ASJC Scopus subject areas

  • Chemical Engineering(all)

Cite this

Newandee, D. A., Reisman, S. S., Bartels, M. N., & De Meersman, R. E. (2003). COPD severity classification using principal component and cluster analysis on HRV parameters. In S. Reisman, R. Foulds, & B. Mantilla (Eds.), Bioengineering, Proceedings of the Northeast Conference (pp. 134-135)

COPD severity classification using principal component and cluster analysis on HRV parameters. / Newandee, D. A.; Reisman, S. S.; Bartels, Matthew N.; De Meersman, R. E.

Bioengineering, Proceedings of the Northeast Conference. ed. / S. Reisman; R. Foulds; B. Mantilla. 2003. p. 134-135.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Newandee, DA, Reisman, SS, Bartels, MN & De Meersman, RE 2003, COPD severity classification using principal component and cluster analysis on HRV parameters. in S Reisman, R Foulds & B Mantilla (eds), Bioengineering, Proceedings of the Northeast Conference. pp. 134-135, Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, Newark, NJ, United States, 3/22/03.
Newandee DA, Reisman SS, Bartels MN, De Meersman RE. COPD severity classification using principal component and cluster analysis on HRV parameters. In Reisman S, Foulds R, Mantilla B, editors, Bioengineering, Proceedings of the Northeast Conference. 2003. p. 134-135
Newandee, D. A. ; Reisman, S. S. ; Bartels, Matthew N. ; De Meersman, R. E. / COPD severity classification using principal component and cluster analysis on HRV parameters. Bioengineering, Proceedings of the Northeast Conference. editor / S. Reisman ; R. Foulds ; B. Mantilla. 2003. pp. 134-135
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