Feature extraction via dynamic PCA for epilepsy diagnosis and epileptic seizure detection

Feature extraction is an important technique for complex, multivariate data containing various attributes. In this paper, we propose new detection schemes to help diagnosing epilepsy and detecting the onset of epileptic seizures. These schemes are based on the dynamic principle component analysis (PCA) approach and on partially extracted features. We propose a detection performance measure for evaluation of performance of the detection schemes. We also introduce a method for determining the threshold of the PC classifier using the normalized partial energy sequence of the extracted features of the training data set. We use partially extracted features to act as a classifier to help diagnosing epilepsy and detecting the onset of epileptic seizures. A publicly available EEG database is employed to evaluate our detection schemes. Our study shows that the proposed detection schemes are very promising in assisting diagnosis of epilepsy and for epileptic seizure detection.