Predictability analysis for an automated seizure prediction algorithm
Document Type
Article
Abstract
Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.
Medical Subject Headings
Adult; Algorithms; Brain Mapping; Diagnosis, Computer-Assisted; Electrodes; Electroencephalography (methods, statistics & numerical data); Electronic Data Processing (methods); Female; Humans; Longitudinal Studies; Male; Middle Aged; Predictive Value of Tests; ROC Curve; Retrospective Studies; Seizures (diagnosis, physiopathology); Sensitivity and Specificity; Time Factors
Publication Date
12-1-2006
Publication Title
Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society
ISSN
0736-0258
Volume
23
Issue
6
First Page
509
Last Page
20
PubMed ID
17143139
Digital Object Identifier (DOI)
10.1097/00004691-200612000-00003
Recommended Citation
Sackellares, J Chris; Shiau, Deng-Shan; Principe, Jose C.; Yang, Mark C.; Dance, Linda K.; Suharitdamrong, Wichai; Chaovalitwongse, Wanpracha; Pardalos, Panos M.; and Iasemidis, Leonidas D., "Predictability analysis for an automated seizure prediction algorithm" (2006). Translational Neuroscience. 1164.
https://scholar.barrowneuro.org/neurobiology/1164