Applying independent component analysis to heart rate and blood pressure variations

H. W. Chiu, Chung-Yi Hsu

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

1 Citation (Scopus)

Abstract

The variations of heart rate (HR) and blood pressure (BP) reflect autonomic control. Most studies used spectral analysis and time-domain statistics to assess autonomic functions. Such methods provide some parameters to represent sympathetic and vagal activities. Independent component analysis (ICA) is a statistical signal processing method for blind separation. Assume that HR and BP pressure variations are linearly composed by some independent hidden signals and these hidden signals represent some meaningful physiological signals such as cardiac nervous outflow and hormonal level. Applying ICA to HR and BP variations signals will be expected to extract these hidden signals. In this study, the HR and BP variations data of six subjects were measured and the beat-to-beat RR intervals, systolic BP, and diastolic BP were considered as the mixed signals to be decomposed. The results from ICA showed that these signals were decomposed to noise component, dominate oscillation component and slow-changed component. Dominate oscillation component is similar to the spectral component observed from traditional spectral analysis but show a de-noised form. The physiological meaning of slow-changed component remains to be further studied. This study shows that ICA will be helpful for HR and BP variation analysis.

Original languageEnglish
Title of host publicationComputers in Cardiology
Pages579-582
Number of pages4
Volume32
DOIs
Publication statusPublished - 2005
EventComputers in Cardiology, 2005 - Lyon, France
Duration: Sep 25 2005Sep 28 2005

Other

OtherComputers in Cardiology, 2005
CountryFrance
CityLyon
Period9/25/059/28/05

Fingerprint

Blood pressure
Independent component analysis
Heart Rate
Blood Pressure
Spectrum analysis
Signal processing
Statistics
Noise
Pressure

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Software

Cite this

Applying independent component analysis to heart rate and blood pressure variations. / Chiu, H. W.; Hsu, Chung-Yi.

Computers in Cardiology. Vol. 32 2005. p. 579-582 1588167.

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

Chiu, HW & Hsu, C-Y 2005, Applying independent component analysis to heart rate and blood pressure variations. in Computers in Cardiology. vol. 32, 1588167, pp. 579-582, Computers in Cardiology, 2005, Lyon, France, 9/25/05. https://doi.org/10.1109/CIC.2005.1588167
Chiu, H. W. ; Hsu, Chung-Yi. / Applying independent component analysis to heart rate and blood pressure variations. Computers in Cardiology. Vol. 32 2005. pp. 579-582
@inproceedings{c9ae913177a44f6798f2f6ed381f6cd5,
title = "Applying independent component analysis to heart rate and blood pressure variations",
abstract = "The variations of heart rate (HR) and blood pressure (BP) reflect autonomic control. Most studies used spectral analysis and time-domain statistics to assess autonomic functions. Such methods provide some parameters to represent sympathetic and vagal activities. Independent component analysis (ICA) is a statistical signal processing method for blind separation. Assume that HR and BP pressure variations are linearly composed by some independent hidden signals and these hidden signals represent some meaningful physiological signals such as cardiac nervous outflow and hormonal level. Applying ICA to HR and BP variations signals will be expected to extract these hidden signals. In this study, the HR and BP variations data of six subjects were measured and the beat-to-beat RR intervals, systolic BP, and diastolic BP were considered as the mixed signals to be decomposed. The results from ICA showed that these signals were decomposed to noise component, dominate oscillation component and slow-changed component. Dominate oscillation component is similar to the spectral component observed from traditional spectral analysis but show a de-noised form. The physiological meaning of slow-changed component remains to be further studied. This study shows that ICA will be helpful for HR and BP variation analysis.",
author = "Chiu, {H. W.} and Chung-Yi Hsu",
year = "2005",
doi = "10.1109/CIC.2005.1588167",
language = "English",
isbn = "0780393376",
volume = "32",
pages = "579--582",
booktitle = "Computers in Cardiology",

}

TY - GEN

T1 - Applying independent component analysis to heart rate and blood pressure variations

AU - Chiu, H. W.

AU - Hsu, Chung-Yi

PY - 2005

Y1 - 2005

N2 - The variations of heart rate (HR) and blood pressure (BP) reflect autonomic control. Most studies used spectral analysis and time-domain statistics to assess autonomic functions. Such methods provide some parameters to represent sympathetic and vagal activities. Independent component analysis (ICA) is a statistical signal processing method for blind separation. Assume that HR and BP pressure variations are linearly composed by some independent hidden signals and these hidden signals represent some meaningful physiological signals such as cardiac nervous outflow and hormonal level. Applying ICA to HR and BP variations signals will be expected to extract these hidden signals. In this study, the HR and BP variations data of six subjects were measured and the beat-to-beat RR intervals, systolic BP, and diastolic BP were considered as the mixed signals to be decomposed. The results from ICA showed that these signals were decomposed to noise component, dominate oscillation component and slow-changed component. Dominate oscillation component is similar to the spectral component observed from traditional spectral analysis but show a de-noised form. The physiological meaning of slow-changed component remains to be further studied. This study shows that ICA will be helpful for HR and BP variation analysis.

AB - The variations of heart rate (HR) and blood pressure (BP) reflect autonomic control. Most studies used spectral analysis and time-domain statistics to assess autonomic functions. Such methods provide some parameters to represent sympathetic and vagal activities. Independent component analysis (ICA) is a statistical signal processing method for blind separation. Assume that HR and BP pressure variations are linearly composed by some independent hidden signals and these hidden signals represent some meaningful physiological signals such as cardiac nervous outflow and hormonal level. Applying ICA to HR and BP variations signals will be expected to extract these hidden signals. In this study, the HR and BP variations data of six subjects were measured and the beat-to-beat RR intervals, systolic BP, and diastolic BP were considered as the mixed signals to be decomposed. The results from ICA showed that these signals were decomposed to noise component, dominate oscillation component and slow-changed component. Dominate oscillation component is similar to the spectral component observed from traditional spectral analysis but show a de-noised form. The physiological meaning of slow-changed component remains to be further studied. This study shows that ICA will be helpful for HR and BP variation analysis.

UR - http://www.scopus.com/inward/record.url?scp=33847122994&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33847122994&partnerID=8YFLogxK

U2 - 10.1109/CIC.2005.1588167

DO - 10.1109/CIC.2005.1588167

M3 - Conference contribution

AN - SCOPUS:33847122994

SN - 0780393376

SN - 9780780393370

VL - 32

SP - 579

EP - 582

BT - Computers in Cardiology

ER -