Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features

Gait rhythm fluctuations are of great importance for automatic neurodegenerative diseases (NDDs) detection. They provide a cost-effective and noninvasive monitoring tool in which their parameters are related to neuromuscular function. This study investigated a new solution based on a set of new symm...

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Vydáno v:Computers in biology and medicine Ročník 120; s. 103736
Hlavní autoři: Ghaderyan, Peyvand, Ghoreshi Beyrami, Seyede Marziyeh
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.05.2020
Elsevier Limited
Témata:
Algorithms
Amyotrophic lateral sclerosis
Chebyshev approximation
Classification
Classifiers
Coding
Datasets
Divergence
Dynamic gait series warping
Euclidean geometry
Gait
Huntington's disease
Huntingtons disease
Internal Medicine
Methods
Movement disorders
Neural coding
Neurodegenerative diseases
Other
Parkinson's disease
Principal components analysis
Signal processing
Symmetry
Time series
Trigonometric functions
Amyotrophic lateral sclerosis
Parkinson's disease
Dynamic gait series warping
Huntington's disease
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí:Gait rhythm fluctuations are of great importance for automatic neurodegenerative diseases (NDDs) detection. They provide a cost-effective and noninvasive monitoring tool in which their parameters are related to neuromuscular function. This study investigated a new solution based on a set of new symmetric features and sparse non-negative least squares (NNLS) coding classifier. Dynamic gait series warping (DGSW), Euclidean, Manhattan, Minkowski, Chebyshev, Canberra distances, and cosine function were used to quantify the amount of divergence between the left and right stride, swing, and stance intervals. The algorithm was evaluated using the gait signals of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis (ALS), 15 patients with Parkinson's disease (PD) and 20 patients with Huntington's disease (HD). The proposed new approach using symmetric features and NNLS technique achieved outstanding accuracies of 98%, 97%, and 95% on the patients with PD, ALS, and HD, respectively. The findings also suggested that the new DGSW, cosine function, and Chebyshev distance, which are designed to dynamically, geometrically, or nonlinearly quantify the similarity between two time series, provide the discriminatory measures to describe how NDDs alter the gait symmetry. In comparison with other studies, combining symmetric features with a sparse NNLS coding classifier can improve the detection accuracy providing an efficient and cost-effective framework for the development of a NDDs detection system. •A set of new distance-based gait symmetric feature is developed for NDDs detection.•Dynamic gait series warping is used to nonlinearly quantify inter-limb deviations.•Compared to SVM, the sparse coding method shows improved performance.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2020.103736