The onset and offset points of VM delimited the analytic region for frequency domain analysis, by using the spectral turbulence analysis approach. The analytic region in VM was preprocessed to extract the first derivative, aiming at removing the high-amplitude low-frequency components. The derived signal was cut into slices to build a power spectral density time-frequency map by applying the short-time Fourier transform. Each data segment was limited in 25 ms, with 2 ms interval between successive segments to assure adequate time-resolution, tapered by a Blackmann-Harris window after mean removal, and zero-padded to 64 points. After Fourier transform of a particular segment, its spectral amplitude was squared to obtain the estimated power spectral density function. Successive power spectral density function estimates in the analytic region were attached in a three-dimensional map. The boundaries of the analytic region (up to 200 ms duration) were placed 25 ms prior to the onset of the VM and to a point on the ST segment 50 ms after the offset of the VM. In the time frequency map, spectral turbulence was studied by comparing sequential spectral estimates. We calculated Pearson's correlation coefficient between adjacent power spectral function estimates throughout ventricular activation, and deployed the correlation coefficients in a time series, of which the mean and the standard deviation of the intersegment spectral correlation (abbreviated as MSC and SSC, respectively) were calculated. Additionally, we calculated the frequency corresponding to 80% of the total area under a particular power spectral function estimate, starting at zero Hz, which practically represented the edge or the border, and deployed the edge frequency, thus calculated, in a time series. The mean and the standard deviation of the electrical transients (abbreviated as MET and SET, respectively) of the edge frequency series were extracted. Power spectral estimates were limited to the range from 0 to 300 Hz in order to avoid interference of high frequency noise during correlation. MSC and SSC were multiplied by 100 to simplify calculations. Normality threshold values have been defined previously as MSC>94, SSC<=6, MET<=78 and The SET<=31. The presence of intraventricular electrical transients (IVET+) was optimally defined when 2 out of 4 variables were outside normality range. We used the above method based on our hypothesis that the presence of high frequency electrical transients, representing underlying electrically unstable myocardial areas, would determine the reduction of intersegment correlation. Likewise, high frequency transients would increase the energy content of a spectral estimate and the shift the spectral border rightward, to a higher frequency.