PhD Dissertation Defense: Ala Tokhmpash

PhD Dissertation Defense: Fractional Order Derivative in Circuits, Systems, and Signal Processing with Specific Application to Seizure Detection

Ala Tokhmpash

Location: Zoom Link

Abstract: Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures, which are brief episodes of involuntary movement that may involve a part or the entire body and are sometimes accompanied by loss of consciousness. It is the third most common neurological disorder in the United States, only after Alzheimer’s disease and stroke. Patients suffering from epilepsy, a brain disorder, can have more than one type of seizure. Seizure detection systems can be life-changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. Prior works on automated seizure detection overwhelmingly either rely solely entirely on domain knowledge, or instead use a black box deep learning model. This thesis aims to integrate machine learning techniques with available seizure detection methods to improve detection performance. In this process, we take advantage of mathematical tools provided by fractional-order derivatives as well as fuzzy entropy concepts. Specifically, 1) we show the effectiveness of fractional order methods (FOM) in representing signals with long-range dependencies 2) using case studies in control and power systems, we further examine the performance of FOM in the presence of parameter uncertainty. 3) using two publicly available data sets of brain signals from multiple patients, we develop a cohesive framework to leverage FOM for extracting features that can be then used by statistical learning methods. 4) following recent works in this field, we generalize the notion of entropy to include the fractional-order case. Combined with the fuzzy sets describing the uncertainty in data, we leverage fractional fuzzy entropy as a robust descriptor of the state of brain signals. Through these case studies, we demonstrate a significant increase in performance accuracy compared to models that do not consider FOM.