Unsupervised profiling of meditation-induced autonomic responses using electrodermal and heart rate variability features

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10258127 RIV/61989100:27620/25:10258127

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised profiling of meditation-induced autonomic responses using electrodermal and heart rate variability features

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Unsupervised profiling of meditation-induced autonomic responses using electrodermal and heart rate variability features

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30401 - Health-related biotechnology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

2025

Číslo periodika v rámci svazku

27

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

24

Strana od-do

nestránkováno

Kód UT WoS článku

001545275400004

EID výsledku v databázi Scopus

2-s2.0-105012197539