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August 22, 2025InformationOpen Access

Physiological State Recognition via HRV and Fractal Analysis Using AI and Unsupervised Clustering

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Authors

GGGalya Georgieva-TsanevaKCKrasimir CheshmedzhievYTYoan-Aleksandar Tsanev

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Overview

This analysis demonstrates effective unsupervised classification of physiological states in participants, suggesting potential for anomaly detection in health monitoring systems.

Key Points

  • Unsupervised clustering of physiological states achieved over 90% consistency across various conditions.
  • Core heart rate variability descriptors, including SDNN and RMSSD, facilitated the classification process and dimensionality reduction.
  • The use of DBSCAN helped identify outliers in physiological data, indicating potential for early anomaly detection.
  • The method successfully classified three physiological conditions, validating its effectiveness against physiological labels with high agreement.

Cite This Study

Georgieva-Tsaneva et al. (2025) studied this question.

synapsesocial.com/papers/68af76a77567bf4f94fef042https://doi.org/10.3390/info16090718
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