Date of Award
Master of Science (MS)
Levi B. Good
This study investigated the possibility of reducing the time required for accurate epileptic seizure detection through a retroactive analysis. Epilepsy is a neurological disorder affecting over 50 million individuals globally and is defined as a disorder which results in two seizures unprovoked by fever or medication. Diagnosis of epilepsy typically involves a monitored stay at an Epilepsy Monitoring Unit (EMU). The monitoring and diagnosis process ranges on average from $35,000 to $40,000 for a single stay, and the patient results from EMU are not instantly available to the patient. The collected electroencephalogram (EEG) must be analyzed by a trained EMU technician before the physician analyzes the data.
The retroactive seizure detection algorithm utilizes Teager-Kaiser energy (TE). TE increases as either a signal’s frequency or amplitude increases and is only dependent on three consecutive samples from the time-domain. The detection algorithm was trained and tested on 37,718 hours of data from 70 male Sprague Dawley rats with a total of 843 recorded seizures. The algorithm resulted in an average sensitivity of 98.1% and an average false positive rate (FPR) of 0.2660 per hour. Current algorithms involve a training stage and perform with a sensitivity between 80% and 98.8% and a FPR between 0.054 and 1 per hour. The study supports TE as a useful measure for seizure detection, and although this algorithm focuses on retroactive seizure detection, the quick response time of TE makes it well suited for real-time seizure detection.
Clary, Samuel, "" (2019). Thesis. 14.