Abstract
Objective.Effective deep brain stimulation (DBS) treatment for Parkinson's disease requires careful surgical targeting and adjustment of stimulation parameters to avoid motor side effects caused by activation of the internal capsule. Currently, patients must self-report side effects during implantation surgery and device programming-a subjective and inconsistent process that may delay optimized treatment or result in suboptimal therapy. Motor evoked potentials (mEP), the use of electromyography to record DBS-induced muscle activation, offer a promising biomarker for objective motor side effect detection.Approach.We present an automated algorithmic procedure for mEP detection and quantification.Main results.First, we design and evaluate a series of signal processing techniques to accurately detect mEP while mitigating the influence of stimulation artifacts and noise, then demonstrate a strategy for integrating multi-channel EMG responses into a single side effect biomarker (the mEP score). Next, we use data from a large patient cohort of intraoperative recordings (N= 54 subthalamic nucleus (STN) leads) to quantify several physiological features of mEP, including their response frequency, latency, amplitude, and waveform similarity properties. Last, we show that the mEP score responds to DBS amplitude and contact configuration parameters in a manner that is consistent with expected STN-capsular anatomy.Significance.The results of this study inform an end-to-end approach for side effect biomarker measurement that could aid the precision and efficiency of surgical targeting and DBS programming.