Patient-specific seizure onset detection.
This article presents an automated, patient-specific method for the detection of epileptic seizure onset from noninvasive electroencephalography. We adopt a patient-specific approach to exploit the consistency of an individual patient's seizure and nonseizure electroencephalograms. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an electroencephalographic epoch, and then determines whether that vector is representative of a patient's seizure or nonseizure electroencephalogram using the support vector machine classification algorithm. Our completely automated method was tested on noninvasive electroencephalograms from 36 pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0+/-3.2 seconds of electrographic onset, and declared 15 false detections in 60 hours of clinical electroencephalography. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset, for example, the injection of a functional imaging radiotracer.
Shoeb
Ali
A
Harvard-MIT Health Sciences and Technology, Medical Engineering and Medical Physics Program, Cambridge, MA, USA. ashoeb@mit.edu
Edwards
Herman
H
Connolly
Jack
J
Bourgeois
Blaise
B
Treves
S Ted
ST
Guttag
John
J
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods; D011930:Reaction Time / Q000502:physiology; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013997:Time Factors