Low Latency Brain Signal Analysis Algorithm to Conserve Battery Life of Deep Brain Stimulation Devices

Team: Eesha Verma

Program: Biomedical Engineering

Parkinson’s disease (PD) is a neurodegenerative disease that affects about 1% of the population older than 65 years of age, and about 10 million people worldwide. The debilitating effects of this disease significantly reduce patient quality of life and can sometimes lead to death. Current neurological surgical procedures designed to treat Parkinson’s disease rely on deep brain stimulation (DBS), a technique that distributes electrical pulses to neural pathways with the goal of reestablishing normal brain function. In existing commercial DBS systems, the device is always actively delivering stimulation pulses, diminishing battery life. The goal of this project is to modify the device so it can more selectively deliver stimulation pulses, conserving battery life and thus reducing the need for future surgeries to replace the battery. Analyzing characteristics of brain signals such as phase, frequency, and amplitude can be useful to determine the abnormality of the signals. The goal of this project is to provide an algorithm that allows the DBS device to be turned on only when abnormal signals are present and turned off when the abnormal signals disappear. In this project, code was developed to detect the instantaneous phase, frequency, and amplitude of the recorded brain signals and was written at a level that decreases code runtime and reduces processing time delay, and thus has a potential for low latency neuromodulation application.

Dr. Yousef Salimpour, Ph.D. (Clinical Sponsor JHMI)
Dr. William S. Anderson, M.D., Ph. D, M.A. (Clinical Sponsor JHMI)


Constanza Miranda, Ph.D. (BME Faculty Mentor)

Team Members

Project Links

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