Recent work has found that a frequency domain and time domain analysis of the heart rate variability signals can provide significant insights into function of the heart in healthy subjects and in patients with heart disease. Patients with congestive heart failure are an important clinical health issue and it is hoped that this work will contribute towards gaining knowledge of this debilitating pathological condition. Our laboratory has recently acquired more than three thousand 24-hour ECG tapes recorded during called Study of Left Ventricular Dysfunction (SOLVD). The SOL VD trial was conducted between 1987-1990 to test the efficacy of a medication called, Enalapril, to treat patients with heart failure. There were an equal number of patients with (group A) and without overt heart failure (group B). The work reported in this thesis describes the development of a hardware and software framework used to analyze the ECG signals recorded on these tapes. Primary objective of this work was to develop and test a system which would assist in analyzing the above tapes so as to examine if there are differences between two groups using the HRV parameters from both frequency and time domain. The research was conducted in three steps: Hardware design, software and algorithm development and finally the validation phase of the design, to test the usefulness of the overall system. The tapes were replayed on a tape recorder and the ECG was digitized at a rate corresponding to 500 samples/second. Labview software was invoked for this task. Secondly a set of algorithms were developed to perform QRS-detection and QT-interval identification. The detection algorithms involved placing critical ECG fiducials onto the ECG waveform through the use of a trained model. The model construction used patient specific pre-annotated data coupled with statistical and genetic algorithm techniques. The beat-to-beat HRV signal was thus generated using the annotation data from the ECG. Frequency domain indices were obtained using power spectral computation algorithms while time domain statistical indices were computed using standard methods. QT-interval algorithms were tested using a set of manually and automatically tagged set of beats from a sample of subjects. For the third part of this research, i.e. validation phase, we set up a test pool of 200 tapes each from patients with overt heart failure and with no heart failure, recorded at the baseline before the subjects entered the study. This phase of the study was conducted with the help of a statistician in a blinded fashion. Our results suggest that there is significant difference between frequency domain and time domain parameters computed from the HRV signals recorded from subjects belonging to group A and group B. The group A patients had a lot of ectopic beats and were challenging to analyze. These results provide a confirmation of our analytical procedures using real clinical data. The QT-analysis of the ECG signals suggest that automatic analysis of this interval is feasible using algorithms developed in this study. / Thesis / Master of Applied Science (MASc)