title: Parkinsonian tremor identification with multiple local field potential feature classification.
abstract: This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.
authors: Department of Cybernetics, Czech Technical University, Prague, Czech Republic. eduard.bakstein@fel.cvut.cz
authors: Eduard
authors: E
authors: Bakstein
authors: Jonathan
authors: J
authors: Burgess
authors: Kevin
authors: K
authors: Warwick
authors: Virginie
authors: V
authors: Ruiz
authors: Tipu
authors: T
authors: Aziz
authors: John
authors: J
authors: Stein
mesh_terms: D046690:Deep Brain Stimulation / Q000379:methods; D004567:Electrodes, Implanted; D004576:Electromyography; D005071:Evoked Potentials / Q000502:physiology*; D005917:Globus Pallidus / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000150:complications* / Q000628:therapy; D013057:Spectrum Analysis; D020531:Subthalamic Nucleus / Q000502:physiology; D013997:Time Factors; D014202:Tremor / Q000175:diagnosis* / Q000209:etiology* / Q000473:pathology / Q000628:therapy