ECG interval measurement and variability analysis has proven to be a useful tool in investigating autonomic nervous system function. Traditionally, the R to R interval has been the thrust of research conducted in this field. QT variability is another topic on which little research has been done.

This thesis uses an algorithm proposed by Dr. Berger, published in 2003. The algorithm stretches the template in the time domain, and composes a sum-squared error function, which is minimized to find the scaling factor for that beat. These scaling factors are compiled into an array, and an interpolated signal is generated of QT interval times. Using Lab VIEW and MATLAB, the algorithm was put into place, and frequency spectral analysis conducted on patients with normal sinus rhythm and those with arrhythmias. This system has been tested statistically, showing a less than 10 percent deviation from a standardized database of QT interval lengths (Physionet MIT/BIH databases) in all cases and less than 5 percent in most. In addition, power spectral analysis was conducted on patients with normal sinus rhythms and those with arrhythmias. However, major differences between patient populations were not observed with the performed techniques.