Application of machine learning to epileptic seizure onset detection and treatment

Epilepsy is a chronic disorder of the central nervous system that predisposes individuals to experiencing recurrent seizures. It affects 3 million Americans and 50 million people world-wide. A seizure is a transient aberration in the brain's electrical activity that produces disruptive physical symptoms such as a lapse in attention and memory, a sensory hallucination, or a whole-body convulsion. Approximately 1 out of every 3 individuals with epilepsy continues to experience frequent seizures despite treatment with multiple anti-epileptic drugs. These intractable seizures pose a serious risk of injury, limit the independence and mobility of an individual, and result in both social isolation and economic hardship. This thesis presents novel technology intended to ease the burden of intractable seizures. At its heart is a method for computerized detection of seizure onset. The method uses machine learning to construct patient-specific classifiers that are capable of rapid, sensitive, and specific detection of seizure onset. The algorithm detects the onset of a seizure through analysis of the brain's electrical activity alone or in concert with other physiologic signals. When trained on 2 or more seizures and tested on 844 hours of continuous scalp EEG from 23 pediatric epilepsy patients, our algorithm detected 96% of 163 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Shoeb, Ali Hossam, 1981-

Advisor dc:contributor.advisor

• John V. Guttag.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/54669

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/54669