Advancing Deep Brain Stimulation: Machine Learning for Intraoperative SubThalamic Nucleus Targeting from MicroElectrode Recordings

Deep Brain Stimulation (DBS) of the SubThalamic Nucleus (STN) is an effective electroceutical therapy for treating motor symptoms in patients with Parkinson’s disease. Accurate placement of the stimulating electrode within the STN is essential for achieving optimal therapeutic outcomes. To this end, MicroElectrode Recordings (MERs) are acquired during surgery to provide intraoperative visual and auditory confirmation of the electrode position. This work introduces a machine learning-based pipeline for real-time classification of MERs to identify the STN during DBS procedures. The pipeline, designed for high classification accuracy and real-time applicability, incorporates interpretable machine learning techniques to ensure compatibility with clinical practices. The performance of a multi-layer perceptron was evaluated both with and without an intermediate artifact removal step applied during data pre-processing. The artifact removal step significantly enhanced classification accuracy from 84.4% to 88.7% (p < 0.001) with a minimal increase in processing time, from 12.5 ms to 14.1 ms per 1-s segment (p < 0.001). The proposed method, with its performance, outdoes the state-of-the-art methods and offers a significant step forward in supporting decision-making during DBS surgeries, promising improved patient outcomes through enhanced accuracy and efficiency.