High-sensitivity acceleration sensor detecting micro-mechanomyogram and deep learning approach for parkinson's disease classification.
Acceleration Sensor
Classification
Deep learning
Mechanomyogram
Parkinson Disease
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 Oct 2024
03 Oct 2024
Historique:
received:
05
03
2024
accepted:
26
09
2024
medline:
3
10
2024
pubmed:
3
10
2024
entrez:
2
10
2024
Statut:
epublish
Résumé
High-sensitivity acceleration sensors have been independently developed by our research group to detect vibrations that are > 10 dB smaller than those detected by conventional commercial sensors. This study is the first to measure high-frequency micro-vibrations in muscle fibers, termed micro-mechanomyogram (MMG) in patients with Parkinson's disease (PwPD) using a high-sensitivity acceleration sensor. We specifically measured the extensor pollicis brevis muscle at the base of the thumb in PwPD and healthy controls (HC) and detected not only low-frequency MMG (< 15 Hz) but also micro-MMG (≥ 15 Hz), which was preciously undetectable using commercial acceleration sensors. Analysis revealed remarkable differences in the frequency characteristics of micro-MMG between PwPD and HC. Specifically, during muscle power output, the low-frequency MMG energy was greater in PwPD than in HC, while the micro-MMG energy was smaller in PwPD compared to HC. These results suggest that micro-MMG detected by the high-sensitivity acceleration sensor provides crucial information for distinguishing between PwPD and HC. Moreover, a deep learning model trained on both low-frequency MMG and micro-MMG achieved a high accuracy (92.19%) in classifying PwPD and HC, demonstrating the potential for a diagnostic system for PwPD using micro-MMG.
Identifiants
pubmed: 39358456
doi: 10.1038/s41598-024-74526-x
pii: 10.1038/s41598-024-74526-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22941Subventions
Organisme : Japan Science and Technology Corporation
ID : JPMJSP2106
Organisme : Japan Society for the Promotion of Science
ID : Number 22K17630
Organisme : Japan Science and Technology Corporation,Japan
ID : JPMJCR21C5
Informations de copyright
© 2024. The Author(s).
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