title: Higher order statistics and neural network for tremor recognition.
abstract: This paper is concerned with the tremor characterization for the purpose of recognition. Three different types of tremor are considered in this paper: the parkinsonian, essential, and physiological. It has been proven that standard second-order statistical description of tremor is not sufficient to distinguish between these three types. Higher order polyspectra based on third- and fourth-order cumulants have been proposed as the additional characterization of the tremor time series. The set of 30 quantities based on the polyspectra has been proposed and investigated as the features for the recognition of tremor. The neural network of the multilayer perceptron structure has been used as a classifier. The results of numerical experiments have proven high efficiency of the proposed approach. The average error of recognition of three types of tremor did not exceed 3%.
authors.affiliation: Institute of Fundamental Electronics, Military University of Technology, Warsaw, Poland.
authors.forename: Jacek
authors.initials: J
authors.lastname: Jakubowski
authors.forename: Krzystof
authors.initials: K
authors.lastname: Kwiatos
authors.forename: Augustyn
authors.initials: A
authors.lastname: Chwaleba
authors.forename: Stanislaw
authors.initials: S
authors.lastname: Osowski
mesh_terms: D003198:Computer Simulation; D005583:Fourier Analysis; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020734:Parkinsonian Disorders / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D014202:Tremor / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology