This thesis is composed of two parts: i) Development of a fully automatic Heart Rate Variability (HRV) analysis method, and ii) development of new methods for ventricular repolarization (T wave) analysis. The first part of this study deals with fully automatic measurement of heart rate variability (HRV) in short term electrocardiograms. In short, HRV analysis is the spectral analysis of the heart rate signal. Presently, all existing HRV analysis programs require user intervention for ectopic beat identification which is essential for reliable HRV analysis. This makes HRV studies in large populations problematic. A fully automatic algorithm to discriminate ventricular and supra-ventricular ectopic beats from normal beats is pre.sented. The method incorporates several approaches and uses three EGG leads. It uses the template matching for the basic morphology check of the QRS complex and the P-wave, the timing information to avoid unnecessary computation and to adjust the thresholds and also looks for a special QRS morphology which is common in ventricular ectopic beats. The method is tested on a set of real ECG recordings and statistically analyzed on the basis of sensitivity and specificity. Its performance using single ECG leads and different triplets of EGG leads is also studied. We have obtained 99% specificity and SVE sensitivity and 98% VE sensitivity and thus concluded that fully automatic HRV analysis is feasible. The second part of this thesis is on ventricular repolarization analysis (T wave analysis). It has been shown that heterogeneity in ventricular repolarization is a mark of abnormality and can be used for risk stratification. Several methods have been proposed to measure this heterogeneity, among which the QT interval measurements are the most popular ones. After a short discussion of the existing methods, we propose three new approaches for T wave analysis, which are aimed to overcome the drawbacks of the existing methods: The spatial and temporal variations in the T wave morphology and the wavefront direction difference between the ventricular depolarization and repolarization waves. All of the descriptors are defined in an ECG decomposition space constructed by Singular Value Decomposition. The spatial variation characterizes the morphology differences between standard leads. The temporal variation measures the change in interlead relations throughout the T wave. The wavefront direction difference quantizes the difference between the progress of the two processes. None of them requires time domain measurements thus avoid the inaccuracies associated with conventional methods. The new methods are compared with the conventional ones in a set of 1100 normal ECGs. The short-term intra-subject reproducibility of the new and the conventional methods is compared in a set of 760 normal (recorded from 76 normal subjects) and 630 abnormal (recorded from 63 HGM patients) EGGs. The new descriptors’ ability to discriminate normal and abnormal EGGs (both in univariate and multivariate models) is also analyzed on the same data set. A two-way blind study conducted on a set of AMI (Acute Myocardial Infarction) patients have shown that the new methods are able to discriminate the high risk group. The conventional methods were shown to be useless in this patient group in a previous study. We have concluded that the new descriptors do not correlate with the conventional ones, are more reproducible, lead to more significant separation between normal and abnormal ECGs in both univariate and multivariate models.