Energy-Efficient Neuromorphic Closed-Loop Modulation System for Parkinson's Disease

Document Type

Conference Proceeding

Publication Date

5-30-2025

Department

Department of Electrical and Computer Engineering

Abstract

Parkinson's Disease (PD) impacts millions globally, causing debilitating motor symptoms. While Closed-Loop Deep Brain Stimulation (CL-DBS) has emerged as a promising treatment, existing systems often suffer from high energy consumption, making them impractical for wearable or implantable devices. This research introduces an innovative neuromorphic approach to enhance CL-DBS performance, utilizing Leaky Integrate-and-Fire (LIF) neuron-based controllers to adaptively modulate stimulation signals based on symptom severity. Two controllers, the on-off LIF and dual LIF models, are proposed, achieving significant reductions in power consumption by 19% and 56%, respectively, while enhancing suppression efficiency by 4.7% and 6.77%. Additionally, this work addresses the scarcity of datasets for PD symptoms by developing a novel dataset featuring neural activity from the subthalamic nucleus (STN), incorporating beta oscillations as key physiological biomarkers. This dataset aims to support further advancements in neuromorphic CL-DBS systems and is openly shared with the research community. By combining energy-efficient neuromorphic controllers with a comprehensive dataset, this study not only advances the technological feasibility of CL-DBS systems for PD treatment but also provides a foundation for personalized and adaptive neuromodulation therapies, paving the way for improved quality of life for individuals with Parkinson's Disease.

Publication Title

Proceedings International Symposium on Quality Electronic Design Isqed

ISBN

9798331509422

Link to Full Text

Share

COinS