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

• Published in: Journal of medical systems Vol. 38; no. 12; pp. 147 - 6
• Main Authors: Khorasani, Abed, Daliri, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.12.2014; Springer Nature B.V
• Subjects: Central nervous system; Classification; Classifiers; Education & Training; Gait; Gait Disorders, Neurologic - classification; Gait Disorders, Neurologic - diagnosis; Gait Disorders, Neurologic - etiology; Health Informatics; Health Sciences; Humans; Intervals; Markov analysis; Markov Chains; Markov models; Medicine; Medicine & Public Health; Normal Distribution; Parkinson Disease - classification; Parkinson Disease - complications; Parkinson Disease - diagnosis; Parkinson's disease; Patients; Statistics for Life Sciences; Walking; Gait classification; Hidden markov model; Movement disorders; Parkinson’s disease
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-014-0147-5

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.