Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio (FDMR) with Application to Seizure Prediction.

The ratio of spectral power in two different bands and relative band power have been shown to be sometimes more discriminative features than the spectral power in a specific band for binary classification of a time-series for seizure prediction. Using auto-regressive modeling, this paper, for the first time, theoretically explains that, for high signal-to-noise ratio (SNR) cases, the ratio features may sometimes amplify the discriminability of one of the two states in a time-series as compared to a band power. This paper, also for the first time, introduces a novel frequency-domain model ratio (FDMR) that can be used to select the two frequency bands. The FDMR computes the ratio of the frequency responses of the two auto-regressive model filters that correspond to two different states. It is shown that the ratio implicitly cancels the effect of change of variance of the white noise that is input to the auto-regressive model in a non-stationary environment for high SNR conditions. It is also shown that under certain sufficient but not necessary conditions the ratio of spectral power and the relative band power can be better discriminators than band power.