An active learning framework for personalized deep brain stimulation

Abstract

Abstract Background: To personalize deep brain stimulation (DBS), we need to identify a link between DBS parameters and the neural response of an individual. The existing approach based on random or empirical sampling (RS) is time-consuming, costly, and practically impossible in a clinical setting. This limits the discovery of novel settings under challenging situations. To address this problem, we have developed a new algorithmic framework based on active learning (AL) that can obtain the best model between the DBS parameters and the brain response while minimizing the number of experiments. Methods: We used a computational Parkinson’s disease model to generate synthetic data. We swept the subthalamic nucleus DBS amplitude, frequency, and pulse width while estimating the globus pallidus internus (GPi) beta (13-30 Hz) power for each DBS parameter. This resulted in 200 different samples. We randomly selected 80% of this data for pool training and reserved the remaining 20% as unseen test data. Using three initial training samples, we trained two linear regression models (based on RS and AL) to obtain a link between DBS parameters and GPi beta power. We iteratively added one training sample at a time to both models based on AL and RS approaches until we had 20 training samples, then tested both models on unseen test data at each iteration and calculated the root mean squared error (RMSE). This process was repeated 1000 times. Results: The mean RMSE of the AL and RS approach was 0.043 and 0.039. Results showed the AL-based model outperformed the RS-based model by showing significantly less errors on the unseen dataset based on a two-sample t-test (p= 2.33e-07, N=1000). Conclusion: We validated that our AL approach outperforms the existing approach in identifying an individualized link between DBS parameters and brain response while minimizing the duration of the experimental procedure. Research Category and Technology and Methods Translational Research: 1. Deep Brain Stimulation (DBS) Keywords: Personalized Deep Brain Stimulation, Active Learning, Machine Learning