This paper evaluates the effectiveness of a wearable device, developed by the
author, to detect different types of epileptic seizures and monitor epileptic patients. The
device uses GSR, Pulse, EMG, body temperature and 3-axis accelerometer sensors to
detect epilepsy. The device first learns the signal patterns of the epileptic patient in ideal
condition. The signal pattern generated during the epileptic seizure, which are distinct from
other signal patterns, are detected and analyzed by the algorithms developed by the author.
Based on an analysis, the device successfully detected different types of epileptic seizures.
The author conducted an experiment on himself to determine the effectiveness of the device
and the algorithms. Based on the simulation results, the algorithms are 100 percent accurate
in detecting different types of epileptic seizures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_38029 |
| Contributors | Khandnor Bakappa, Pradeepkumar (author), Agarwal, Ankur (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 110 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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