LAUSR

During Deep Brain Stimulation (DBS) surgery for treating Parkinson's disease, detecting the Subthalamic Nucleus (STN) and its sub-territory called the Dorsolateral Oscillatory Region (DLOR) is crucial for adequate clinical outcomes. Currently, the detection is based on human experts, often guided by supervised machine learning detection algorithms. This procedure depends on the knowledge and experience of particular experts and on the amount and quality of the labeled data used for training the machine learning algorithms. In this paper, to circumvent such dependence and the inevitable bias introduced by the training data, we present a data-driven unsupervised algorithm for detecting the STN and the DLOR during DBS surgery based on an agnostic modeling approach. Given measurements, we extract new features and compute a variant of the Mahalanobis distance between these features. We show theoretically that this distance enhances the differences between measurements with different intrinsic characteristics. Incorporating the new features and distance into a manifold learning method, called Diffusion Maps, gives rise to a representation that is consistent with the underlying factors that govern the measurements. Since this representation does not rely on rigid modeling assumptions and is obtained solely from the measurements, it facilitates a broad range of detection tasks; here, we propose a specification for STN and DLOR detection during DBS surgery. We present detection results on 25 sets of measurements recorded from 16 patients during surgery. Compared to a supervised algorithm, our unsupervised method demonstrates similar results in detecting the STN and superior results in detecting the DLOR.