Deepak Joshi1, Aayushi Khajuria2, Pradeep Joshi3. 1. Center for Biomedical Engineering, Indian Institute of Technology, Delhi, India. 2. Department of Electrical Engineering, Graphic Era University, Dehradun, India. Electronic address: aayushikhajuria@gmail.com. 3. Quantum Business School, Roorkee, India.
Abstract
BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various analysis and classification approaches using spatiotemporal gait variables have been presented earlier in classifying Parkinson's gait. In this paper, we present a wavelet transform based representation of spatiotemporal gait variables to explore the potential of such representation in the identification of Parkinson's gait. METHODS: Here, we present wavelet analysis as an alternate method and show that wavelet analysis combined with support vector machine (SVM) can produce efficient classification accuracy. Computationally simplified features are extracted from the wavelet transformation and are fed to support vector machine for Parkinson's gait identification. We have assessed various gait parameters namely stride interval, swing interval, and stance interval (from both legs) to observe the best single parameter for such classification. RESULTS: By employing wavelet decomposition of the gait variables as an alternate method for the identification of Parkinson's subjects, the classification accuracy of 90.32% (Confidence Interval; 74.2%-97.9%) has been achieved, at par to recently reported accuracy, using only one gait parameter. Left stance interval performed equally good to Right swing interval showing classification accuracy of 90.32%. The classification accuracy improved to 100% when all the gait parameters from left leg were put together to form a larger feature vector. We have shown that Haar wavelet performed significantly better than db2 wavelet (p = 0.05) for certain gait variables e.g., right stride time series. The results show that wavelet analysis is a promising approach in reducing down the required number of gait variables, however at the cost of increased computations in wavelet analysis. CONCLUSIONS: In this work a wavelet transform approach is explored to classify Parkinson's subjects and healthy subjects using their gait cycle variables. The results show that the proposed method can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.
BACKGROUND AND OBJECTIVE: The automatic noninvasive identification of Parkinson's disease (PD) is attractive to clinicians and neuroscientist. Various analysis and classification approaches using spatiotemporal gait variables have been presented earlier in classifying Parkinson's gait. In this paper, we present a wavelet transform based representation of spatiotemporal gait variables to explore the potential of such representation in the identification of Parkinson's gait. METHODS: Here, we present wavelet analysis as an alternate method and show that wavelet analysis combined with support vector machine (SVM) can produce efficient classification accuracy. Computationally simplified features are extracted from the wavelet transformation and are fed to support vector machine for Parkinson's gait identification. We have assessed various gait parameters namely stride interval, swing interval, and stance interval (from both legs) to observe the best single parameter for such classification. RESULTS: By employing wavelet decomposition of the gait variables as an alternate method for the identification of Parkinson's subjects, the classification accuracy of 90.32% (Confidence Interval; 74.2%-97.9%) has been achieved, at par to recently reported accuracy, using only one gait parameter. Left stance interval performed equally good to Right swing interval showing classification accuracy of 90.32%. The classification accuracy improved to 100% when all the gait parameters from left leg were put together to form a larger feature vector. We have shown that Haar wavelet performed significantly better than db2 wavelet (p = 0.05) for certain gait variables e.g., right stride time series. The results show that wavelet analysis is a promising approach in reducing down the required number of gait variables, however at the cost of increased computations in wavelet analysis. CONCLUSIONS: In this work a wavelet transform approach is explored to classify Parkinson's subjects and healthy subjects using their gait cycle variables. The results show that the proposed method can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.