OBJECTIVE: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multichannel or multisensor data analysis of gait dynamics in Parkinson's disease. METHOD: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multisensor time series of gait force between Parkinson's disease and healthy control cohorts. RESULTS: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross validations. CONCLUSION: Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease. SIGNIFICANCE: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.
OBJECTIVE: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multichannel or multisensor data analysis of gait dynamics in Parkinson's disease. METHOD: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multisensor time series of gait force between Parkinson's disease and healthy control cohorts. RESULTS: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross validations. CONCLUSION: Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease. SIGNIFICANCE: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.
Authors: Lazzaro di Biase; Alessandro Di Santo; Maria Letizia Caminiti; Alfredo De Liso; Syed Ahmar Shah; Lorenzo Ricci; Vincenzo Di Lazzaro Journal: Sensors (Basel) Date: 2020-06-22 Impact factor: 3.576