Faculty Advisor or Committee Member

Taskin Padir, Advisor

Faculty Advisor or Committee Member

Bryan McLaughlin, Advisor

Faculty Advisor or Committee Member

Michael Gennert, Committee Member

Identifier

etd-091213-120514

Abstract

"A combined hardware and software platform for ambulatory seizure onset detection is presented. The hardware is developed around commercial off-the-shelf components, featuring ADS1299 analog front ends for electroencephalography from Texas Instruments and a Broadcom ARM11 microcontroller for algorithm execution. The onset detection algorithm is a patient-specific support vector machine algorithm. It outperforms a state-of-the-art detector on a reference data set, with 100% sensitivity, 3.4 second average onset detection latency, and on average 1 false positive per 24 hours. The more comprehensive European Epilepsy Database is then evaluated, which highlights several real-world challenges for seizure onset detection, resulting in reduced average sensitivity of 93.5%, 5 second average onset detection latency, and 85.5% specificity. Algorithm enhancements to improve this reduced performance are proposed."

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Robotics Engineering

Project Type

Thesis

Date Accepted

2013-09-12

Accessibility

Unrestricted

Subjects

machine learning, svm, embedded, epilepsy, seizure, eeg

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