Interictal Spike Detection in EEG using Time Series Classification

Epilepsy is the most rampant neurological disorder that is diagnosed for around 50 million people in the world. We obtain beneficial information related to the different physiological states of the brain via the electroencephalogram (EEG). S pikes in an EEG recording serves as a major parameter for diagnosing epilepsy in an individual [2]. This research aims to efficiently identify and classify Spikes in an EEG Signal using multilayer filtration of noises to predict epilepsy. The methodology explained in the following research introduces two layers of a classifier to classify time series into spikes and nonspikes given a channel of EEG recording. The advantages of the proposed method are its computational simplicity, easy algorithmic implementation, and immunity to artifacts, noise, and implicit characteristics of EEG. As opposed to a naive approach of sliding windows for the classification of time series, the following method uses a layer of the preliminary classifier with very high classification sensitivity and a layer of classification, where the focus is given on high classification accuracy. The detection algorithm uses mathematical operations and thresholding in the first layer and uses the support vector machine(SVM) classifier in the second layer to classify the time series windows into spikes and non-spikes in an EEG recording.