Author Type

Graduate Student

Date of Award

2017

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Agarwal, Ankur

Abstract

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.

Extent

110 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2017.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00004937

Format

pdf

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