An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
27 Sep 2024
27 Sep 2024
Historique:
received:
05
02
2024
accepted:
11
09
2024
medline:
28
9
2024
pubmed:
28
9
2024
entrez:
27
9
2024
Statut:
epublish
Résumé
Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.
Identifiants
pubmed: 39333541
doi: 10.1038/s41597-024-03878-w
pii: 10.1038/s41597-024-03878-w
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1051Informations de copyright
© 2024. The Author(s).
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