ArticleEntropy (Basel, Switzerland)2020
Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Selective Motor Entropy Modulation and Targeted Augmentation for the Identification of Parkinsonian Gait Patterns Using Multimodal Gait Analysis.Life (Basel, Switzerland) · 2026Article
- Assessment of Postural Stability in Semi-Open Prisoners: A Pilot Study.Journal of clinical medicine · 2025Article
- A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis.Medical & biological engineering & computing · 2025Article
- Decision tree for severity assessment of neurodegenerative diseases using possibility approach and gait dynamics.Scientific reports · 2025Article
- Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.Scientific reports · 2025Article
- Application of multidimensional gait feature fusion algorithm in gait assessment for patients with knee osteoarthritis.Frontiers in bioengineering and biotechnology · 2025Article
- Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000.Frontiers in neurology · 2025Review
- Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024Review
- Gait analysis under the lens of statistical physics.Computational and structural biotechnology journal · 2022Review
- Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.Digital healthReview
- Article
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Authors and funding
3 authors.
Funding
Abstract
The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and
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Registered trials
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