Name: Seyede Mahya Safavi
Chair: Prof. Pai Chou
Date: February 20th, 2020
Time: 10:00 AM
Location: EH 3404
Committee: Prof. Pai Chou (Chair), Prof. Beth Lopour, Prof. Phillip Sheu
Title: “Novel Monitoring, Detection, and Optimization Techniques for Neurological Disorders”
The advancement in chronically implanted neural recording devices has led to advent of assisting devices for rehabilitation and restoring lost sensorimotor functions in patients suffering from paralysis. Electrocorticogram (ECoG) signal can record high-Gamma sub-band activity known to be related to hand movements. In the first part of this work, we Propose a finger movement detection technique based on ECoG source localization. In fact, the finger flexion and extension are originating in slightly different areas of motor cortex. The origin of the brain activity is used as the distinctive feature for decoding the finger movement.
The real-time implementation of brain source localization is challenging due to extensive iterations in the existing solutions. In the second part of this work, we have proposed two techniques to reduce the computational complexity of the Multiple Signal Classification (MUSIC) algorithm. First the cortex surface is parsed into several regions. Next, a novel nominating procedure will pick a number of regions to be searched for brain activity. In the second step, an electrode selection technique based on the Cramer-Rao bound of the errors is proposed to select the best set of an arbitrary number of electrodes. The proposed techniques lead to 90% reduction in computational complexity while maintaining a good concordance in terms of localization error compared to regular MUSIC algorithm.
Epilepsy is a neurological disorder with multiple comorbid conditions including cardiovascular and respiratory disorders. The cardiovascular imbalance is of great importance since the mechanisms of Sudden Unexpected Death in Epilepsy (SUDEP) is still unknown. The ictal tachycardia is the most well-known cardiac imbalance during the seizure. In the third part of this dissertation, we used an optical sensor called photoplethysmogram (PPG) to investigate the variations in ictal blood flow in limbs. Six different features related to hemodynamics were derived from PPG pulse morphology. A consistent pattern of ictal change was observed across all the subjects/seizures. These variations suggest an increase in vascular resistance due to an increase in sympathetic tone. The timing analysis of the PPG features revealed some PPG feature variations can precede the ictal tachycardia by 50 seconds. These features were used to train a neural network based on Long Short Term Memory LSTM architecture for automatic seizure detection. We were able to reduce the False Alarm rate by 50% compared to other heart rate variability based detectors.