Patient-specific seizure onset detection

Ali Shoeb; Herman Edwards; Jack Connolly; Blaise Bourgeois; Ted Treves; John Guttag

doi:10.1109/IEMBS.2004.1403183

Patient-specific seizure onset detection

Conf Proc IEEE Eng Med Biol Soc. 2004:2006:419-22. doi: 10.1109/IEMBS.2004.1403183.

Authors

Ali Shoeb¹, Herman Edwards, Jack Connolly, Blaise Bourgeois, Ted Treves, John Guttag

Affiliation

¹ Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, USA.

PMID: 17271701
DOI: 10.1109/IEMBS.2004.1403183

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.