Closed-loop deep brain stimulation based on a stream-clustering system. (15th July 2019) - Camara, C.; Warwick, K.; Bruña, R.; Aziz, T.; Pereda, E.
Highlights: A closed-loop deep brain stimulation system is presented. Neural connectivity features are explored to improve classification. A stream clustering approach is proposed. Performance of the proposed model achieves a 100% of accuracy. Abstract: Idiopathic Parkinsons disease (PD) is currently the second most important neurodegenerative disease in incidence. Deep brain stimulation (DBS) constitutes a successful and necessary therapy; however, the continuous stimulation it provides can be associated with multiple side effects. DBS uses an implanted pulse generator that delivers, through a set of electrodes, electrical stimulation to the target area, normally the Sub Thalamic Nucleus. Recently, Closed-loop DBS has emerged as a promising new strategy, where the device stimulates only when necessary, thereby reducing any adverse effects. Here, we present a Closed-loop DBS system for PD, which is able to recognize, with 100% accuracy, when the patient is going to enter into the tremor phase, thus allowing the device to stimulate only in such cases. The expert system has been designed and implemented within the data stream mining paradigm, suitable for our scenario since it can cope with continuous data of a theoretical infinite length and with a certain variability, which uses the synchronization among the neural population within the Sub Thalamic Nucleus as the continuous data stream input to the system.