Classification of Neurodegenerative Diseases via Topological Motion Analysis—A Comparison Study for Multiple Gait Fluctuations

Yan, Yan; Omisore, Olatunji Mumini; Xue, Yu-Cheng; Li, Hui; Liu, Qiu-Hua; Nie, Zedong; Fan, Jianping; Wang, Lei

doi:10.1109/access.2020.2996667

Cited by 24 publications

(13 citation statements)

References 63 publications

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“…Classification accuracy of 96.39% is obtained with sensitivity and specificity of 96.77% 95.89%, respectively. Moreover, stride intervals obtained from VGRF signals of gait in neurodegenerative disease database has been employed with LDA, [105] Random Forest using TMA framework, [106] SVM, [107] HMM, [90] Decision tree [124] to discriminate PD from HD, ALS, and healthy subjects. Khoury et al [89] obtained 90% accuracy in differentiating PD from ALS, HD, and healthy groups with a K-NN classifier.…”

Section: Lstmmentioning

confidence: 99%

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Balakrishnan¹,

Medikonda²,

Namboothiri³

et al. 2022

Eng. Sci.

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Gait analysis, a way of assessing the manner of walking, is considered a significant criterion in diagnosing movement disorder. Various factors contribute to the alterations in gait patterns, of which neurodegenerative related disorders play a major role. Subjects affected by Parkinson's disease (PD) suffer from numerous gait-related disturbances, eventually worsening the Quality of Life. Artificial intelligence-based tools have shown great interest in computer-assisted diagnosis with the recent advancements in technology. This review article aims at portraying a novel collective approach of accenting every facet of PD gait by emphasizing the role of quantitative gait analysis and state-of-art technologies in the betterment of clinical diagnosis. The paper includes all the relevant research works (2014-2021) regarding PD assessments categorized as 1) Classification of PD and Healthy subjects, 2) PD severity prediction, and 3) Freezing of Gait detection by only considering gait modality as the mode of assessment.

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Section: Lstmmentioning

confidence: 99%

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Balakrishnan¹,

Medikonda²,

Namboothiri³

et al. 2022

Eng. Sci.

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“…Motion capture technology has become a new medical tool that assists doctors and therapists during the diagnosis and treatment of their patients. Gait analysis helps determine a neurodegenerative disease [98], evaluate different treatment outcomes for cerebral palsy [99], or identify individualized therapeutic strategies for running injuries [100]. The skeleton data are also used for motion monitoring and injury prevention [101].…”

Section: Health Carementioning

confidence: 99%

Content-Based Management of Human Motion Data: Survey and Challenges

et al. 2021

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Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.

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“…The TDA technique adopts a persistent homology [56], [57] tool to describe the point clouds, providing a novel description of the structure of the point clouds and topological properties of the phase space. The nonlinear dynamics analysis with topological descriptions has been used in wheeze detection [58], heart dynamics analysis toward arrhythmia detection [59], gait dynamics analysis toward neurodegenerative disease discrimination [60], [61], EEG-based dynamics analysis toward brain state recognition [62], [63], [64], [65], [66], [67], [68], [69] and plenty of time series classification applications [70], [71], [72]. This work proposes a topological nonlinear dynamics analysis approach toward EEG-based emotion recognizing as a complement of the phase space information, namely topological EEG nonlinear dynamics analysis (TEEGNDA).…”

Section: Introductionmentioning

confidence: 99%

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Yan,

Wu,

et al. 2022

Preprint

Self Cite

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Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

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Classification of Neurodegenerative Diseases via Topological Motion Analysis—A Comparison Study for Multiple Gait Fluctuations

Cited by 24 publications

References 63 publications

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Content-Based Management of Human Motion Data: Survey and Challenges

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

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