Browsing by Subject "Closed-loop"
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Item Optimizing electrical brain stimulation for seizure disorders(2017-03) Nagaraj, VivekApproximately 1% of the world population is afflicted with Epilepsy. For many patients, antiepileptic drugs do not fully control seizures. Electrical brain stimulation therapies have been effective in reducing seizure rates in some patients. While current neuromodulation devices provide a benefit to patients, efficacy can be improved by optimizing brain stimulation so that the therapy is tuned on a patient by patient basis. One optimization approach is to target deep brain regions that strongly modulate seizure prone regions. I will present data on the effects of stimulation of two different anatomical regions for seizure control, and establish my experimental platform for testing closed-loop algorithms. There are two general methods to implementing closed-loop algorithms to modulate neural activity: 1) Model-free algorithms that require a learning period to establish an optimal mapping between neural states and best therapeutic parameters, and 2) Model-based algorithms that use forward predictions of the neural system to determine the appropriate stimulation therapy to be administered. In this thesis, I will propose and test two closed-loop control schemes to control the brain activity to prevent epileptogenic activity while reducing stimulation energy. I will also present techniques to remove stimulation artifacts so that neural biomarkers can be measured while simultaneously applying stimulation. The methods I will present could potentially be implemented in next generation electrical brain stimulation hardware for seizure disorders and other neurological diseases.Item Using Quantified Motor Behavior Outcomes to Improve Deep Brain Stimulation in Parkinson’s Disease(2020-06) Louie, KennethDeep brain stimulation (DBS) is a highly effective therapeutic option for Parkinson’s disease (PD). However, it can take 50 or more hours to obtain stimulation settings that optimally treat a patient’s symptoms. Additionally, axial symptoms, such as gait, are not adequately treated in the long term. In my work I explore the use of quantified motor behavior outcomes to reduce the time needed to obtain optimal stimulation parameters, and to develop a novel stimulation delivery approaches to better treat gait. First, I tested a Bayesian optimization approach to quickly and accurately model the input/output response of rigidity to stimulation frequency. I found, for PD patients that have a high degree of rigidity, Bayesian optimization models their response needing fewer samples than a traditional trial-and-error approach. Next, I tested a novel closed-loop stimulation delivery approach that delivered short duration pulse trains at specific phases of gait. I found that the patients that respond strongly to this type of stimulation delivery have a worse gait with their clinical settings. Overall, many patients saw small changes to their gait with this approach. Lastly, I analyzed the effects of turning stimulation on and off on gait. I found that a repeated measures of gait with short duration, 1 minute, wash-in and -out can detect significant changes. This is in contrast to previous reports that significant changes are only seen between 30-60 minutes. Through these studies I demonstrate the use of quantified motor behavior outcomes to improve DBS for PD.