Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait
Open accessAbstract
Differential diagnosis between Idiopathic Parkin-son's disease (IPD) and Vascular Parkinsonism (VaP) is a difficult task, especially early in the disease. There is growing evidence to support the use of gait assessment in diagnosis and management of movement disorder diseases. The aim of this study is to evaluate the effectiveness of some machine learning strategies in distinguishing IPD and VaP gait. Wearable sensors positioned on both feet were used to acquire the gait data from 15 IPD, 15 VaP, and 15 healthy subjects. A comparative classification analysis was performed by applying two supervised machine learning algorithms: Multiple Layer Perceptrons (MLPs) and Deep Belief Networks (DBNs). The decisional space was composed of the gait variables, with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top-ranked in an error incremental analysis. In the classification task of characterizing parkinsonian gait by distinguishing between patients (IPD+VaP) and healthy control, from the all strides classification of the gait performed by the person, high accuracy (93% with or without MoCA) was obtained for both algorithms. In the classification task of the two groups of patients (VaP vs. IPD), DBN classifier achieved higher performance (73% with MoCA). To the best of our knowledge, this is the first study on gait classification that includes a VaP group. DBN classifiers are not frequently applied in literature to similar studies, but the result…
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Fernandes et al. employed DBNs to support gait assessment in diagnosis of Parkinson's and movement disorder diseases, confirming that DBN-based approaches are suitable for the task.
“Regarding gait recognition, Fernandes et al. [21] employed DBNs to support gait assessment in the diagnosis of Parkinson's and movement disorder diseases. The authors employed wearable sensors to extract features from the subjects and performed a comparative classification analysis of parkinsonian gait, confirming that DBN-based approaches are suitable for the task.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [21]
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- Authors
- Fernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., Ferreira, C., Gama, J., Erlhagen, W., & Bicho, E.
- Affiliations
- University of Minho (PT)
- INESC TEC (PT)
- Centro ALGORITMI (PT)
- Venue
- 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 2024-2030
- OpenAlex
- W2912379919
- Cited by
- 17
- References
- 33
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 4 · 2020: 1 · 2019: 1
- FWCI
- 13.92
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- parkinsonism, idiopathic Parkinson's disease, vascular parkinsonism, gait analysis, wearable sensors, deep belief network, multilayer perceptron, Montreal Cognitive Assessment, differential diagnosis, movement disorders, gait classification
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