A Physiology-Based Seizure Detection System for Multichannel EEG
Journal
PLoS ONE
Journal Volume
8
Journal Volume
8
Journal Issue
6
Journal Issue
6
Start Page
e65862
ISSN
1932-6203
Date Issued
2013-06-14
Author(s)
Shen, Chia-Ping
Liu, Shih-Ting
Zhou, Wei-Zhi
Lin, Feng-Seng
Lam, Andy Yan-Yu
Sung, Hsiao-Ya
Lin, Jeng-Wei
Kao, Jui-Hung
Abstract
Background:Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.Methodology:This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.Principal Findings:We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.Conclusion:We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system. © 2013 Shen et al.
Other Subjects
adult; algorithm; article; automated pattern recognition; clinical article; controlled study; digital filtering; electric potential; electroencephalogram; entropy; epileptic discharge; female; genetic algorithm; human; male; neurophysiology; phase reversal; sample entropy; seizure; spike; spike matching algorithm; support vector machine; temporal lobe epilepsy; waveform; Adult; Aged; Aged, 80 and over; Electroencephalography; Female; Humans; Male; Middle Aged; Models, Biological; ROC Curve; Seizures; Support Vector Machines; Young Adult
Type
journal article