The Need
Current clinical methods for assessing tremor severity in patients with Parkinson’s disease and other movement disorders rely heavily on subjective, qualitative observations, such as evaluating hand-drawn spirals or observing motor tasks. These approaches lack precision, are difficult to standardize across clinicians, and provide no direct link to underlying neural activity. There is a critical need for an objective, real-time, and quantitative method for measuring tremor characteristics directly from deep brain signals to better guide diagnosis and therapy.

The Technology
Developed by OSU researchers, this technology provides a system and method for quantitatively predicting tremor amplitude and frequency using deep brain local field potential (LFP) signals recorded from directional deep brain stimulation (DBS) electrodes. By identifying twin dominant peaks in the beta-band of the LFP signal’s frequency spectrum, the system computes the tremor frequency from their difference and uses machine learning models to predict tremor amplitude. The method incorporates noise reduction filters, spectral analysis (e.g., FFT), and feedback algorithms that enable integration with closed-loop DBS systems to optimize stimulation in real time.

Commercial Applications
• Quantitative diagnostic tools for movement disorders
• Digital health solutions for remote tremor monitoring
• Clinical research tools for movement disorder studies

Benefits/Advantages
• Objective Assessment: Replaces subjective scoring with real-time, data-driven tremor quantification
• Closed-Loop Integration: Enables automated DBS adjustment based on measured tremor metrics
• Robust Signal Processing: Uses FFT, Savitzky-Golay filters, and machine learning for noise-robust predictions
• Versatile Use: Applicable across a range of neurological movement disorders

Patent application filed