Detecting Neonatal Seizures using Short Time Fourier Transform and Frechet Distance

Recently there has been an increase in the number of long-term cot-bed EEG systems being implemented in clinical practice in order to monitor neurological development of neonatal patients. Consequently a significant research effort has been made in the development of automatic EEG data analysis tools including but not limited to seizure detection as seizure frequency and/or intensity are one of the most important indicators of brain development. In this paper we propose to evaluate time dependent power spectral density using short time Fourier transform and using Frechet distance measure to detect presence and/or absence of seizures. We propose to use three different distance measures as they capture different properties of the corresponding PSD matrices. We evaluate the performance of the proposed algorithms using real data set obtained in the NICU of the McMaster University Hospital. In order to benchmark performance of our proposed techniques we trained and tested a support vector machine (SVM) classifier.