title: Patient-specific seizure onset detection.
abstract: This paper presents an automated, patient-specific method for the detection of epileptic seizure onsets from noninvasive EEG. We adopt a patient-specific approach to exploit the consistency of an individual patient's seizure and non-seizure EEG. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an EEG epoch, and then determines whether that vector is representative of a patient's seizure or non-seizure EEG using the support-vector machine classification algorithm. Our completely automated method was tested on non-invasive EEG from thirty-six pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0+/-3.2 seconds following electrographic onset, and declared 15 false-detections in 60 hours of clinical EEG. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset; for example, the injection of an imaging radiopharmaceutical or stimulation of the vagus nerve.
authors.affiliation: Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Shoeb
authors.forename: Herman
authors.initials: H
authors.lastname: Edwards
authors.forename: Jack
authors.initials: J
authors.lastname: Connolly
authors.forename: Blaise
authors.initials: B
authors.lastname: Bourgeois
authors.forename: Ted
authors.initials: T
authors.lastname: Treves
authors.forename: John
authors.initials: J
authors.lastname: Guttag