Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning.
LSTM
electrical stimulation
machine learning
tremor
Journal
Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874
Informations de publication
Date de publication:
05 Jan 2023
05 Jan 2023
Historique:
received:
10
10
2022
revised:
23
12
2022
accepted:
30
12
2022
entrez:
21
1
2023
pubmed:
22
1
2023
medline:
22
1
2023
Statut:
epublish
Résumé
Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (
Identifiants
pubmed: 36673255
pii: e25010114
doi: 10.3390/e25010114
pmc: PMC9858124
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : European Union's Horizon 2020
ID : 779982
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