CURRENT EVENTS... Ph.D. Research Proposal - Study of Gas Bubbles Stabilized by Surfactants for Use as Ultrasound Contrast Agents and Drug Carriers Seminar - Technology and the Treatment of Cardiac Arrhythmias: Complex Tools Designed to Meet the Complexities of Physiology Ph.D. Thesis Defense - Manipulation of Microparticles using a Piezoelectric Actuator Seminar - Neurophysiological, Computational, and Engineering Approaches in Cardiorespiratory Control Special Seminar - Extracting Hand Kinematics from Non-invasively Acquired Neural Signals Seminar - Locomotor Recovery After Body Weight Supported Treadmill Training (BWSTT) in Contused Rats EVENTS Archive EVENT GALLERY Archive NEWS & EVENTS Home BIOMED Home

Speaker(s):
Kevin J. Freedman
Advisor: Karen Moxon, Ph.D.

Details:
Epilepsy is the second most common neurologic disorder which is characterized by recurrent and spontaneous seizures. Seizures occur unpredictably which makes everyday tasks such as driving and working extremely difficult resulting in a reduced quality of life. Although pharmaceutical treatment works well for approximately 70% of patients, 30% of epilepsy patients live with this disease unless they are eligible for surgical removal of the epileptic brain tissue responsible for initiating the seizures. Epilepsy monitoring units measure the electrical activity of the brain using electroencephalography (EEG) in an attempt to locate the epileptic brain tissue. However since seizures occur unpredictably and are generally infrequent, long recording times generates massive quantities of data which must be reviewed by neurologists. Automating this process using a seizure detection algorithm will ultimately save time and money, allow for better and safer care of patients, and provide a better diagnostic tool. Although seizure detection has been well studied in the laboratory and clinic, a widely accepted algorithm has not been developed largely due to the inability to perform as well as a neurologist. In order to improve performance, multiple algorithms were used as feature extractors and were implemented into a support vector machine (SVM) algorithm. The proposed algorithm was optimized and tested using an animal model of epilepsy as well as human epilepsy data obtained from Hahnemann University Hospital. The results of this analysis showed that the multi-measurement SVM algorithm performed better than any single measurement. Additionally, the SVM algorithm performed better than the commercially available XLTEK algorithm by obtaining a sensitivity of 98.9% and a positive predictive value of 25.5%.

Biosketch:

Directions:

The Bossone Research Enterprise Center is located at the corner of 32nd and Market Streets.