On predicting epileptic seizures from intracranial electroencephalography.
10.1007/s13534-017-0008-5
- Author:
Yongseok YOO
1
Author Information
1. Department of Computer and Information Communications, Hongik University, D407, 2639 Sejong-ro, Sejong-si 30016, Korea. yyoo@hongik.ac.kr
- Publication Type:Original Article
- Keywords:
Predictive analytics;
Seizure prediction;
EEG;
Cross-validation;
Personalized healthcare
- MeSH:
Drug Resistant Epilepsy;
Electrocorticography*;
Electroencephalography;
Epilepsy*;
Humans;
Seizures;
Sensitivity and Specificity;
Support Vector Machine
- From:
Biomedical Engineering Letters
2017;7(1):1-5
- CountryRepublic of Korea
- Language:English
-
Abstract:
This study investigates the sensitivity and specificity of predicting epileptic seizures from intracranial electroencephalography (iEEG). A monitoring system is studied to generate an alarm upon detecting a precursor of an epileptic seizure. The iEEG traces of ten patients suffering from medically intractable epilepsy were used to build a prediction model. From the iEEG recording of each patient, power spectral densities were calculated and classified using support vector machines. The prediction results varied across patients. For seven patients, seizures were predicted with 100% sensitivity without any false alarms. One patient showed good sensitivity but lower specificity, and the other two patients showed lower sensitivity and specificity. Predictive analytics based on the spectral feature of iEEG performs well for some patients but not all. This result highlights the need for patient-specific prediction models and algorithms.
