ArticleCognitive neurodynamics2019
Analysis of heart rate signals during meditation using visibility graph complexity.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From visibility graphs to cognition.Frontiers in network physiology · 2026Article
- Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions.Sensors (Basel, Switzerland) · 2025Article
- Small-world networks propensity in spontaneous speech signals of Alzheimer's disease: visibility graph analysis.Scientific reports · 2025Article
- Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.Brain informatics · 2025Article
- A novel nonlinear bispectrum analysis for dynamical complex oscillations.Cognitive neurodynamics · 2024Article
- Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.Brain informatics · 2023Article
- Computational methods of EEG signals analysis for Alzheimer's disease classification.Scientific reports · 2023Article
- Visibility Graph Analysis of Heartbeat Time Series: Comparison of Young vs. Old, Healthy vs. Diseased, Rest vs. Exercise, and Sedentary vs. Active.Entropy (Basel, Switzerland) · 2023Article
- Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective.Biomedical engineering online · 2023Review
- Complex network based models of ECoG signals for detection of induced epileptic seizures in rats.Cognitive neurodynamics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.