Faculty Advisor

Taskin Padir

Faculty Advisor

Bryan McLaughlin

Faculty Advisor

Michael Gennert

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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