Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease

Abstract

When pharmaceutical treatments for Parkinson’s Disease (PD) are no longer effective, Deep Brain Stimulation (DBS) surgery, a procedure that entails the stimulation of the Subthalamic Nucleus (STN), is another treatment option. However, the success rate of this surgery heavily relies on the precise location of the STN, as well as the correct positioning of the stimulation electrode. In order to ensure the correct location, Micro-Electrode Recordings (MERs) are analyzed. During surgery, MERs capture brain signals while inserted in the brain, receiving different brain activity depending on the crossed brain area. The location of the STN is guaranteed when brain signals from MERs meet certain criteria. Nevertheless, MER signals are sensitive to various artifacts coming from machinery or other electrical equipment in the operating theater; patient activity; and electrode motion. These all lower the signal-to-noise ratio of the MER signals. MER signals are stochastic, multicomponent, transient, and non-stationary in nature, and they contain multi-unit neural activity in the form of spikes and artefacts. Thus, accurately defining that MERs are located in the STN is not an easy task. This work analyzes relevant features from MER, based on analyzing spike activity and local field signals. Six different classification algorithms are used, together with the optimal input feature selection. The algorithms are trained using supervised Leave-One-Out Cross-Validation. MER data were collected in a real scenario from 14 PD patients during DBS implantation surgery. The dataset is publicly available. The results derived from the use of this method show an accuracy of up to 100% in detecting where the MER electrode is located in the STN brain area.