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.

Creator

• Kindle, Alexander Lawrence

Contributors

• Gennert, Michael A.
• Padir, Taskin
• McLaughlin, Bryan

Degree

• MS

Unit

• Robotics Engineering

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-091213-120514

Keyword

• Embedded
• EEG
• Machine Learning
• epilepsy
• SVM
• seizure

Advisor

• McLaughlin, Bryan
• Padir, Taskin

Committee

• Gennert, Michael A.

Defense date

• 2013-09-19

Year

• 2013

Date created

• 2013-09-12

Resource type

• Thesis

Rights statement

• In Copyright