Automated classification of five seizure onset patterns from intracranial electroencephalogram signals. Issue 6 (June 2020)

• Record Type: Journal Article
• Authors: Makaram, Navaneethakrishna; von Ellenrieder, Nicolás; Tanaka, Hideaki; Gotman, Jean
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 6(2020:Jun.)
• Page Start: 1210
• Page End: 1218
• Publication Date: 2020-06
• Subjects: Ictal onset -- Ictal spread -- Intracranial EEG -- Multiscale entropy -- Machine learning; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.02.011
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: We studied the various seizure onset patterns in drug resistant epilepsy using intracranial EEG. Time domain and Complexity based features resulted in better characterization of the patterns. Support vector machine - Error-Correcting Output Codes results in a classification accuracy of 80.7% with the combined features. Abstract: Objective: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we propose a system to automatically classify five seizure onset patterns from intracerebral EEG signals. Methods: The EEG was segmented by clinicians indicating the start and end time of each seizure onset pattern, the channels involved at onset and the seizure onset pattern. Twelve features that represent the time domain characteristics and signal complexity were extracted from 663 seizures channels of 24 patients. The features were used for classification of the patterns with support vector machine - Error-Correcting Output Codes (SVM-ECOC). Three patient groups with a similar number of seizure segments were created, and one group was used for testing and the rest for training. This test was repeated by rotating the testing and training data. Results: The feature space formed by both time domain and multiscale sample entropy features perform well in classification of the data. An overall accuracy of 80.7% was obtained with these features and a linear kernel of SVM-ECOC. Conclusions: The seizure onset patterns consist of varied time and complexity characteristics. It is possible to automatically classify various seizure onset patterns very similarly to visual classification. Significance: The proposed system could aid the medical team in assessing intracerebral EEG by providing an objective classification of seizure onset patterns.