Abstract
The quantitation of the variability in cardiovascular signals provides information about
the autonomic neural regulation of the heart and the circulatory system. Several factors
have an indirect effect on these signals as well as artifacts and several types of noise are
contained in the recorded signal. The dynamics of RR and QT interval time series have
also been analyzed in order to predict a risk of adverse cardiac events and to diagnose
them.
An ambulatory measurement setting is an important and demanding condition for the
recording and analysis of these signals. Sophisticated and robust signal analysis schemes
are thus increasingly needed. In this thesis, essential points related to ambulatory data
acquisition and analysis of cardiovascular signals are discussed including the accuracy
and reproducibility of the variability measurement. The origin of artifacts in RR interval
time series is discussed, and consequently their effects and possible correction procedures
are concidered. The time series including intervals differing from a normal sinus rhythm
which sometimes carry important information, but may not be as such suitable for an
analysis performed by all approaches. A significant variation in the results in either intra-
or intersubject analysis is unavoidable and should be kept in mind when interpreting the
results.
In addition to heart rate variability (HRV) measurement using RR intervals, the dy-
namics of ventricular repolarization duration (VRD) is considered using the invasively
obtained action potential duration (APD) and different estimates for a QT interval taken
from a surface electrocardiogram (ECG). Estimating the low quantity of the VRD vari-
ability involves obviously potential errors and more strict requirements. In this study,
the accuracy of VRD measurement was improved by a better time resolution obtained
through interpolating the ECG. Furthermore, RTmax interval was chosen as the best QT
interval estimate using simulated noise tests. A computer program was developed for the
time interval measurement from ambulatory ECGs.
This thesis reviews the most commonly used analysis methods for cardiovascular vari-
ability signals including time and frequency domain approaches. The estimation of the
power spectrum is presented on the approach using an autoregressive model (AR) of
time series, and a method for estimating the powers and the spectra of components is
also presented. Time-frequency and time-variant spectral analysis schemes with applica-
tions to HRV analysis are presented. As a novel approach, wavelet and wavelet packet
transforms and the theory of signal denoising with several principles for the threshold
selection is examined. The wavelet packet based noise removal approach made use of an
optimized signal decomposition scheme called best tree structure. Wavelet and wavelet
packet transforms are further used to test their effciency in removing simulated noise
from the ECG. The power spectrum analysis is examined by means of wavelet transforms,
which are then applied to estimate the nonstationary RR interval variability. Chaotic
modelling is discussed with important questions related to HRV analysis.ciency in removing simulated noise
from the ECG. The power spectrum analysis is examined by means of wavelet transforms,
which are then applied to estimate the nonstationary RR interval variability. Chaotic
modelling is discussed with important questions related to HRV analysis.
| Identifer | oai:union.ndltd.org:oulo.fi/oai:oulu.fi:isbn951-42-5214-4 |
| Date | 09 April 1999 |
| Creators | Tikkanen, P. (Pauli) |
| Publisher | University of Oulu |
| Source Sets | University of Oulu |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/publishedVersion |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess, © University of Oulu, 1999 |
| Relation | info:eu-repo/semantics/altIdentifier/pissn/0355-3191, info:eu-repo/semantics/altIdentifier/eissn/1796-220X |
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