A Physiology- Based Seizure Detection System for Multichannel EEG

作者：Shen Chia Ping; Liu Shih Ting; Zhou Wei Zhi; Lin Feng Seng; Lam Andy Yan Yu; Sung Hsiao Ya; Chen Wei; Lin Jeng Wei; Chiu Ming Jang^*; Pan Ming Kai; Kao Jui Hung; Wu Jin Ming; Lai Feipei

来源：PLos One, 2013, 8(6): e65862.

DOI：10.1371/journal.pone.0065862

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.

• 出版日期2013-6-14