Deep Brain Stimulation Signal Classification using Deep Belief Networks

An approach to modeling complex real-world data such as biomedical signals is to develop pattern recognition techniques and robust features that capture the relevant information. In this paper, we use a deep belief network (DBN) to predict subcortical structures of patients with Parkinson's disease based on microelectrode records (MER) obtained during deep brain stimulation (DBS). We report on experiments using a data set involving 52 MER for the structures: zona incerta (Zi), subthalamic nucleus (STN), thalamus nucleus (TAL), and substantia nigra (SNR). The results show that our chosen features and network architecture produces a 99.5% accuracy of detection and classification of the subcortical structures under study. Based on the results we conclude that deep belief networks could be used to predict subcortical structure-mainly the STN for neurostimulation.