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HMM for Classification of Parkinson’s Disease Based on the Raw Gait Data

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Abstract

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.

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Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran

    Abed Khorasani & Mohammad Reza Daliri

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  1. Abed Khorasani
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  2. Mohammad Reza Daliri
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Correspondence to Mohammad Reza Daliri.

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Khorasani, A., Daliri, M.R. HMM for Classification of Parkinson’s Disease Based on the Raw Gait Data. J Med Syst 38, 147 (2014). https://doi.org/10.1007/s10916-014-0147-5

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