ArticleBioengineering (Basel, Switzerland)2022
EMD-Based Method for Supervised Classification of Parkinson's Disease Patients Using Balance Control Data.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Interpretable Biomechanical Feature Selection for VR Exercise Assessment Using SHAP and LDA.Sensors (Basel, Switzerland) · 2026Article
- Classification of fall risk in Parkinson's disease using empirical mode decomposition and machine learning.Frontiers in bioengineering and biotechnology · 2026Article
- Automatic differentiation of voluntary and tremulous motion using ensemble empirical mode decomposition and convolutional Bi-directional LSTM.Scientific reports · 2025Article
- Featured Papers in Computer Methods in Biomedicine.Bioengineering (Basel, Switzerland) · 2024Article
- Automatic Transcranial Sonography-Based Classification of Parkinson's Disease Using a Novel Dual-Channel CNXV2-DANet.Bioengineering (Basel, Switzerland) · 2024Article
- Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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Abstract
There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson's disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster-Sahfer formalism method has an accuracy of 96.51%.
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