A new feature for the classification of non-stationary signals based on the direction of signal energy in the time frequency domain

The detection of seizure activity in electroencephalogram (EEG) segments is very important for the classification and localization of epileptic seizures. The evolution of a seizure in an EEG usually appears as a train of non uniformly spaced spikes and/or as piecewise linear frequency modulated signals. If a seizure is present, then the energy of the EEG is concentrated along the time axis and the frequency axis in the time frequency plane. However, in the absence of a seizure, the energy of the EEG signal is uniformly distributed along all directions in the time frequency plane. Based on this observation, we propose a new approach for the detection of a seizure. In this paper, we develop a new feature that exploits the direction of the energy of the signal in the time frequency domain to distinguish between seizures and non-seizures in an EEG. Our experimental results indicate the superiority of the proposed approach over other conventional time frequency approaches; for example, the proposed feature set achieves a classification accuracy of 98.25% by only using five features.