Abstract
Neuro-degenerative disease is a common progressive nervous system disorder that leads to serious clinical consequences. Gait rhythm dynamics analysis is essential for evaluating clinical states and improving quality of life for neuro-degenerative patients. The magnitude of stride-to-stride fluctuations and corresponding changes over time—gait dynamics—reflects the physiology of gait, in quantifying the pathologic alterations in the locomotor control system of health subjects and patients with neuro-degenerative diseases. Motivated by algebra topology theory, a topological data analysis-inspired nonlinear framework was adopted in the study of the gait dynamics. Meanwhile, the topological representation–persistence landscapes were used as input of classifiers in order to distinguish different neuro-degenerative disease type from healthy. In this work, stride-to-stride time series from healthy control (HC) subjects are compared with the gait dynamics from patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and Parkinson’s disease (PD). The obtained results show that the proposed methodology discriminates healthy subjects from subjects with other neuro-degenerative diseases with relatively high accuracy. In summary, our study is the first attempt to provide a topological representation-based method into the disease classification with gait rhythms measured from the stride intervals to visualize gait dynamics and classify neuro-degenerative diseases. The proposed method could be potentially used in earlier interventions and state monitoring.
Highlights
Neuro-degenerative disease is a common progressive disorder of the nervous system, which might lead to the tremor of limbs, jaw or face, and stiffness of slowing of movement [1]
We explore the stride-to-stride intervals as the gait-phase representation to study the gait dynamics for neuro-degenerative diseases
The point clouds are considered as in some abstract space; Filtration Extraction: the point clouds are studied with the simplicial complex theory, the filtrations are achieved for each corresponding space from the point clouds; Barcodes Generation: from the filtration the birth–death intervals for each homology are extracted, which can be illustrated by the Barcodes; Persistence Diagram Generation: the Barcodes can be represented by the persistence diagrams, from which the persistence landscape features can be acquired; Persistence Landscape Feature Acquiring: the persistence landscape features are used as the input as a Gaussian Naive Bayesian classifier toward the classification task
Summary
Neuro-degenerative disease is a common progressive disorder of the nervous system, which might lead to the tremor of limbs, jaw or face, and stiffness of slowing of movement [1]. The neurodegenerative disease symptoms usually emerge slowly and cause movement problems and difficulty with walking. While the gait abnormality as a deviation of walking may reflect different disorder patterns, gait analysis is an essential tool to assess neuro-degenerative disease [2,3,4]. In [5], Kamruzzaman uses two basic temporal-spatial gait parameters (stride length and cadence) as input features and support vector machine method to analyze the cerebral palsy gait. The authors of [6] reported multiple regression normalization strategies that incorporated physical properties and self-selected speed for Parkinson’s Disease Gait analysis. In [7], Wu used a Sensors 2020, 20, 2006; doi:10.3390/s20072006 www.mdpi.com/journal/sensors
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