Unsupervised profiling of meditation-induced autonomic responses using electrodermal and heart rate variability features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15410%2F25%3A73631433" target="_blank" >RIV/61989592:15410/25:73631433 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/25:10258127 RIV/61989100:27620/25:10258127
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2590123025025502" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025025502</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2025.106481" target="_blank" >10.1016/j.rineng.2025.106481</a>
Alternative languages
Result language
angličtina
Original language name
Unsupervised profiling of meditation-induced autonomic responses using electrodermal and heart rate variability features
Original language description
Background and MotivationMeditation practices influence the autonomic nervous system (ANS), as reflected in electrodermal activity (EDA) and heart rate variability (HRV), though individual responses vary. This study aimed to explore whether unsupervised clustering can uncover distinct physiological patterns during guided meditation, aiding personalized assessment and biofeedback development.Materials and MethodsEDA and HRV were recorded from 14 healthy participants (8 men, 6 women) during guided meditation. Signals were preprocessed via filtering, normalization, and segmentation into 3-minute windows. Extracted features included tonic and phasic EDA components, skin conductance responses (SCRs), and HRV metrics (e.g., RMSSD, SDNN). Fuzzy C-means clustering was applied to identify physiological response subgroups. The optimal number of clusters was determined using the Davies–Bouldin index and silhouette scores.ResultsUnsupervised FCM clustering was used to explore physiological responses to meditation. Although clustering yielded four participant-level and five measurement-level groups, post hoc interpretation revealed three overarching response profiles—arousal, balance, and relaxation—based on common feature trends. These interpreted profiles synthesize the clustering results into more accessible representations of individual autonomic variability.DiscussionThe clusters reflect heterogeneous ANS responses to meditation, suggesting it does not induce a uniform physiological state. Unsupervised learning offers an objective approach to profiling individual responses, supporting personalized meditation and biofeedback.ConclusionFuzzy clustering revealed distinct autonomic patterns during meditation, demonstrating potential for personalized neurotechnology and informing tailored mindfulness interventions based on physiological feedback.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30401 - Health-related biotechnology
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
2025
Issue of the periodical within the volume
27
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
24
Pages from-to
nestránkováno
UT code for WoS article
001545275400004
EID of the result in the Scopus database
2-s2.0-105012197539