N2GNet tracks gait performance from subthalamic neural signals in Parkinson's disease.

ABSTRACT: Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). The LFP data were acquired when eighteen PWP performed stepping in place, and the ground reaction forces were measured to track their weight shifts representing gait performance. By exhibiting a stronger correlation with weight shifts c

SUBMITTER: Choi JW 

PROVIDER: S-EPMC11581115 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature