Characterization and application of analysis methods for ECG and time interval variability data

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 intraor 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 dynamics 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 variability 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 variability 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 applications 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 efficiency 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.