Designing Patient-Specific Seizure Detectors From Multiple Frequency Bands of Intra-cranial EEG Using Support Vector Machines

Automatic seizure detection is becoming popular in modern epilepsy monitoring units since it assists diagnostic monitoring and reduces manual review of large volumes of EEG recordings. In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded by a dense Micro-Electrode Array (MEA). The MEA is capable of recording at a very high sampling rate (30 KHz) producing an avalanche of time series data. We explore subsets of this data to build seizure detectors – we discuss several methods for extracting univariate and bivariate features from the channels and study the effectiveness of using Support Vector Machines (SVMs) for constructing the model. Future work involves design of more robust seizure detectors using other features, detection and classification of artifacts and understanding the generalization properties of the models.