Open AccessMachine Learning Based Epileptic Seizure Detection for Responsive Neurostimulator System Optimization
Author(s) -
Yangchicheng Shen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1453/1/012089
Subject(s) - epilepsy , electroencephalography , epileptic seizure , computer science , artificial intelligence , feature extraction , feature (linguistics) , pattern recognition (psychology) , signal (programming language) , set (abstract data type) , machine learning , psychology , neuroscience , linguistics , philosophy , programming language
This paper proposes a novel method of identifying the time of epileptic seizure happening on patients by employing feature extraction and machine learning-based classification on Electroencephalogram (EEG) signal collected from a closed-loop interface implanted in the brain of patients. The closed-loop device was served as a neurostimulator which introduced stimuli to epilepsy patients when detecting the occurrence of seizure. A set of multiple time- and frequency-domain features are extracted from intracranial electroencephalography recordings of 7 subjects with epilepsy. Trained and tested on the extracted features, an ensemble of machine learning models with parameter tuning achieves an area under the curve (AUC) score of 0.99.