J Med Syst - HMM for classification of Parkinson's disease based on the raw gait data.

Tópicos

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Resumo

The central nervous system (CNS) plays an important role in regulation of human gait. Parkinson's disease (PD) is a common neurodegenerative disease that may cause neurophysiologic change in the CNS and as a result change the gait cycle duration (stride interval). This article used the Hidden Markov Model (HMM) with Gaussian Mixtures to separate the patients with PD from healthy subjects. The results showed that the performance of the HMM classifier in classifying the gait data corresponding to 16 healthy and 15 PD subjects is comparable to the results obtained from the least squares support vector machine (LS-SVM) classifier. In this study, the leave-one-out cross-validation method was used to evaluate the performance of each classifier. The HMM method could effectively separate the gait data in terms of stride interval obtained from healthy subjects and PD patients with an accuracy rate of 90.3 % . All in all, the results showed that the proposed method can be used for distinguishing PD patients from healthy subjects based on the gait data classification.

Resumo Limpo

central nervous system cns play import role regul human gait parkinson diseas pd common neurodegen diseas may caus neurophysiolog chang cns result chang gait cycl durat stride interv articl use hidden markov model hmm gaussian mixtur separ patient pd healthi subject result show perform hmm classifi classifi gait data correspond healthi pd subject compar result obtain least squar support vector machin lssvm classifi studi leaveoneout crossvalid method use evalu perform classifi hmm method effect separ gait data term stride interv obtain healthi subject pd patient accuraci rate result show propos method can use distinguish pd patient healthi subject base gait data classif

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