Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain: Dissertation

Ilkka Korhonen

Research output: ThesisDissertationCollection of Articles

Abstract

Cardiovascular variability signals provide information about the functioning of the autonomous nervous system and other physiological sub-systems. Because of large inter- and intra-subject variability, sophisticated data analysis methods are needed to gain this information. An important approach for analysing signals is the analysis in the frequency domain. In this thesis, spectral analysis of cardiovascular variability signals was addressed by two different approaches. The first approach was based on univariate spectral analysis. The novelty of the approach is the quantification of the shift in spectral power within a frequency band. Three different estimators for the spectral shift were compared. The band-wise mean and median frequencies were found to provide better performance than the parameter used in earlier studies, namely central frequency. The band-wise median frequency was successfully applied to real clinical data. In the other approach multivariate closed-loop analysis of the cardiovascular system was studied. A framework based on linear time series modelling and spectral decomposition was presented. The application of multivariate autoregressive (MAR) modelling on real cardiovascular data was addressed in detail, and a method for overcoming the problem of correlating noise sources in MAR modelling was applied successfully. A non-causal model for controlling the effect of respiration on cardiovascular system was proposed. Practical considerations of applying multivariate linear time series modelling to real cardiovascular data were discussed. Methods were demonstrated using real data.
Original languageEnglish
QualificationDoctor Degree
Awarding Institution
  • Tampere University of Technology (TUT)
Supervisors/Advisors
  • Turjanmaa, Väinö, Supervisor, External person
  • Nieminen, Hannu, Advisor, External person
Award date12 Sep 1997
Place of PublicationEspoo
Publisher
Print ISBNs951-38-5066-8
Electronic ISBNs951-38-5067-6
Publication statusPublished - 1997
MoE publication typeG5 Doctoral dissertation (article)

Fingerprint

blood
cardiovascular system
spectral analysis
modeling
time series
nervous system
respiration
analysis
rate
method
decomposition

Keywords

  • blood pressure
  • heart rate
  • spectral analysis
  • spectral decomposition
  • cardiovascular system

Cite this

Korhonen, Ilkka. / Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain : Dissertation. Espoo : VTT Technical Research Centre of Finland, 1997. 96 p.
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title = "Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain: Dissertation",
abstract = "Cardiovascular variability signals provide information about the functioning of the autonomous nervous system and other physiological sub-systems. Because of large inter- and intra-subject variability, sophisticated data analysis methods are needed to gain this information. An important approach for analysing signals is the analysis in the frequency domain. In this thesis, spectral analysis of cardiovascular variability signals was addressed by two different approaches. The first approach was based on univariate spectral analysis. The novelty of the approach is the quantification of the shift in spectral power within a frequency band. Three different estimators for the spectral shift were compared. The band-wise mean and median frequencies were found to provide better performance than the parameter used in earlier studies, namely central frequency. The band-wise median frequency was successfully applied to real clinical data. In the other approach multivariate closed-loop analysis of the cardiovascular system was studied. A framework based on linear time series modelling and spectral decomposition was presented. The application of multivariate autoregressive (MAR) modelling on real cardiovascular data was addressed in detail, and a method for overcoming the problem of correlating noise sources in MAR modelling was applied successfully. A non-causal model for controlling the effect of respiration on cardiovascular system was proposed. Practical considerations of applying multivariate linear time series modelling to real cardiovascular data were discussed. Methods were demonstrated using real data.",
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author = "Ilkka Korhonen",
note = "Project code: TTET952",
year = "1997",
language = "English",
isbn = "951-38-5066-8",
series = "VTT Publications",
publisher = "VTT Technical Research Centre of Finland",
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Korhonen, I 1997, 'Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain: Dissertation', Doctor Degree, Tampere University of Technology (TUT), Espoo.

Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain : Dissertation. / Korhonen, Ilkka.

Espoo : VTT Technical Research Centre of Finland, 1997. 96 p.

Research output: ThesisDissertationCollection of Articles

TY - THES

T1 - Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain

T2 - Dissertation

AU - Korhonen, Ilkka

N1 - Project code: TTET952

PY - 1997

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N2 - Cardiovascular variability signals provide information about the functioning of the autonomous nervous system and other physiological sub-systems. Because of large inter- and intra-subject variability, sophisticated data analysis methods are needed to gain this information. An important approach for analysing signals is the analysis in the frequency domain. In this thesis, spectral analysis of cardiovascular variability signals was addressed by two different approaches. The first approach was based on univariate spectral analysis. The novelty of the approach is the quantification of the shift in spectral power within a frequency band. Three different estimators for the spectral shift were compared. The band-wise mean and median frequencies were found to provide better performance than the parameter used in earlier studies, namely central frequency. The band-wise median frequency was successfully applied to real clinical data. In the other approach multivariate closed-loop analysis of the cardiovascular system was studied. A framework based on linear time series modelling and spectral decomposition was presented. The application of multivariate autoregressive (MAR) modelling on real cardiovascular data was addressed in detail, and a method for overcoming the problem of correlating noise sources in MAR modelling was applied successfully. A non-causal model for controlling the effect of respiration on cardiovascular system was proposed. Practical considerations of applying multivariate linear time series modelling to real cardiovascular data were discussed. Methods were demonstrated using real data.

AB - Cardiovascular variability signals provide information about the functioning of the autonomous nervous system and other physiological sub-systems. Because of large inter- and intra-subject variability, sophisticated data analysis methods are needed to gain this information. An important approach for analysing signals is the analysis in the frequency domain. In this thesis, spectral analysis of cardiovascular variability signals was addressed by two different approaches. The first approach was based on univariate spectral analysis. The novelty of the approach is the quantification of the shift in spectral power within a frequency band. Three different estimators for the spectral shift were compared. The band-wise mean and median frequencies were found to provide better performance than the parameter used in earlier studies, namely central frequency. The band-wise median frequency was successfully applied to real clinical data. In the other approach multivariate closed-loop analysis of the cardiovascular system was studied. A framework based on linear time series modelling and spectral decomposition was presented. The application of multivariate autoregressive (MAR) modelling on real cardiovascular data was addressed in detail, and a method for overcoming the problem of correlating noise sources in MAR modelling was applied successfully. A non-causal model for controlling the effect of respiration on cardiovascular system was proposed. Practical considerations of applying multivariate linear time series modelling to real cardiovascular data were discussed. Methods were demonstrated using real data.

KW - blood pressure

KW - heart rate

KW - spectral analysis

KW - spectral decomposition

KW - cardiovascular system

M3 - Dissertation

SN - 951-38-5066-8

T3 - VTT Publications

PB - VTT Technical Research Centre of Finland

CY - Espoo

ER -