Chronic Monitoring and Longitudinal Efficacy of Neuromodulation for Neurological Movement Disorders
Modern chronic stimulation therapies with sensing capability offer a unique opportinuty to effectively manage the symptoms of a disease while also providing an understanding of the progression of neurological disorders. I design and implement a framework for monitoring the long-term effectiveness of deep brain stimulation. Using this framework we compare several aDBS algorithms for managing essential tremor symptoms. We also show that the beta band biomarker used to decode movement is sufficiently stable over time fo the aDBS paradgim to remain effective for many months. In a second implementation focused on live distributed data collection we demonstrate our framework for optimizing adaptive deep brain stimulation remotely in the patient's home. We also develop a method using this framework to quantify the long-term accuracy of neural based linear discriminat decoders. We demonstrate it's accuracy in the case of an adaptive deep brain stimulation aplogrithm for Parkinsons disease. This work serves as a first step towards the data collection and longitudinal analysis capabilites needed to make adaptive deep brain stimulation a therapy feasible in the chronic at home environment, and facilitate a deeper understanding of the longitudinal effects of neurorogical movement disorders.
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