2026-09-19T21:54:50Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/546692022-01-13T07:53:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

John V. Guttag. Shoeb, Ali Hossam, 1981- Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology 2010-04-28T17:17:43Z 2010-04-28T17:17:43Z 2009 2009 http://hdl.handle.net/1721.1/54669 607331139 Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 157-162). 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. (cont.) In this thesis we also discuss how our detector can be embedded within a low power, implantable medical device to enable the delivery of just-in-time therapy that has the potential to either eliminate or attenuate the clinical symptoms associated with seizures. Finally, we report on the in-hospital use of our detector to enable delay-sensitive therapeutic and diagnostic applications. We demonstrate the feasibility of using the algorithm to control the Vagus Nerve Stimulator (an implantable neuro stimulator for the treatment of intractable seizures), and to initiate ictal SPECT (a functional neuroimaging modality useful for localizing the cerebral site of origin of a seizure). by Ali Hossam Shoeb. Ph.D. 162 p. eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Harvard University--MIT Division of Health Sciences and Technology. Application of machine learning to epileptic seizure onset detection and treatment Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="60e3e3f8-4cbb-4f41-8577-34bbedab9aba"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Application of machine learning to epileptic seizure onset detection and treatment</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Shoeb, Ali Hossam, 1981-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Harvard University--MIT Division of Health Sciences and Technology.</Keyword> <Abstract>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&apos;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&apos;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.</Abstract> <Abstract>(cont.) In this thesis we also discuss how our detector can be embedded within a low power, implantable medical device to enable the delivery of just-in-time therapy that has the potential to either eliminate or attenuate the clinical symptoms associated with seizures. Finally, we report on the in-hospital use of our detector to enable delay-sensitive therapeutic and diagnostic applications. We demonstrate the feasibility of using the algorithm to control the Vagus Nerve Stimulator (an implantable neuro stimulator for the treatment of intractable seizures), and to initiate ictal SPECT (a functional neuroimaging modality useful for localizing the cerebral site of origin of a seizure).</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>