Visibility Graph Analysis of Heartbeat Signals for Meditation and Health Studies

A PDF document authored by Sabin Gautam discusses the application of the natural visibility graph method (NVGM) to analyze non-ergodic signals, such as heartbeats, with an inverse power law index μ ranging from 1 to ∞. The study uses diffusion entropy analysis (DEA) with stripes to identify fractional Gaussian noise (FGN) contributions and reports a significant scaling δ reduction for sick patients compared to healthy ones. The file is 55.0 KB and was last updated on 2026-05-29.

Use Cases

• Comparing scaling δ values between sick and healthy patients based on heartbeat signals.
• Identifying fractional Gaussian noise (FGN) contributions in physiological signals using diffusion entropy analysis (DEA) with stripes.
• Analyzing the effect of meditation on scaling δ reduction in non-ergodic signals.
• Applying the natural visibility graph method (NVGM) to generate homogeneous networks from time-series data.
• Investigating the connection between ergodicity-breaking crucial events and macroscopic quantum effects using a master equation approach.

Strengths

• The study provides a specific methodological focus on NVGM and DEA with stripes.
• It reports a significant difference in scaling δ between sick and healthy patient groups.
• The document is openly licensed under CC-BY-4.0.

Limitations

• The dataset's actual data content, column structure, and row count are unknown.
• The file size is 55.0 KB, indicating a very limited scope, likely a single research paper.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Freshness

Last updated 2026-05-29 04:45:03; freshness should be verified.