Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis

Prabhu, Prince R.; Karunakar, A. K.; Anitha, H; Pradhan, N.

doi:10.1016/j.patrec.2018.05.006

Cited by 41 publications

(40 citation statements)

References 10 publications

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“…For the accuracy test, the SVM algorithm was applied on the extracted data. For data separation and for the high dimensional space, the two separate parallel hyperplanes were constructed for maximal separating hyperplanes on each side in SVM [26]. Due to its capability and efficacy, the SVM was very convenient for accessing a large amount of data classification [27].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Monitoring Movements of Ataxia Patient by Using UWB Technology

Zilani

Al‐Turjman²,

Khan

et al. 2020

Sensors

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Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Monitoring Movements of Ataxia Patient by Using UWB Technology

Zilani

Al‐Turjman²,

Khan

et al. 2020

Sensors

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“…After the replacement of time intervals, we use the right-foot stride-interval signals from the HC, ALS, HD, and PD groups in the experiments. In this work, we consider the classification performances in a one-vs-one setting as in former studies like [10,46]. The involve classification groups are the HC, ALS, HD and PD groups, we consider the binary classification performance of HC group versus ALS group, HC group versus HD group, and HC group versus PD group.…”

Section: Experiments Setupmentioning

confidence: 99%

“…The recurrence plot analysis, and Poincaré plot analysis [53] enables the visualization of the evolution of a dynamical system in the phase space, which are useful for the identification of the hidden patterns. These methods have been adopted in the gait nonlinear dynamics analysis in [46].…”

Section: Nonlinear Analysis Of Gait Dynamicsmentioning

confidence: 99%

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Yan

Ivanov

Omisore

et al. 2020

Sensors

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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.

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“…Significant differences were reported in features such as the LZC and the crossentropy, which allow to disriminate between HC subjects and PD patients. In [17] the authors classified 13 PD patients, 13 Amyotrophic lateral sclerosis patients and 13 Huntington patients and 13 HC subjects using data obtained from force-sensitive resistors [18]. The authors computed NLD features such as the shannon entropy, the recurrence rate, and recurrence quantification analysis (RQA).…”

Section: State Of the Artmentioning

confidence: 99%

Nonlinear dynamics and Poincaré sections to model gait impairments in different stages of Parkinson’s disease

et al. 2020

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Parkinson's Disease is a progressive disorder of the nervous system that affects the motor system of the patients, producing several impairments in muscles and limbs. One of the major manifestations of the disease appears in gait, and typically causes disability of the patients. Gait assessment appears as an useful tool to support the diagnosis process and to evaluate the neurological state of the patients. The gait of the patients is mainly evaluated from signals captured with inertial sensors attached to the limbs of the patients, where kinematics features are commonly computed. On the other hand, there are non-linear effects of the gait process that cannot be properly characterized with the kinematic features. This study proposes the use several non-linear dynamics features to assess the gait impairments of Parkinson's patients. We consider classical non-linear features such as the correlation dimension, the largest Lyapunov exponent, and the Hurst exponent, among others. In addition we propose a novel non-linear analysis based on applying a Gaussian mixture model to find clusters in Poincaré sections. The non-linear dynamics features are used to discriminate between Parkinson's patients and healthy subjects, and

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Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis

Cited by 41 publications

References 10 publications

Monitoring Movements of Ataxia Patient by Using UWB Technology

Monitoring Movements of Ataxia Patient by Using UWB Technology

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation

Nonlinear dynamics and Poincaré sections to model gait impairments in different stages of Parkinson’s disease

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