DOI: 10.3390/s26216742 ISSN: 1424-8220

A Feasibility Study for Integrating Schumann Resonance Monitoring, Geomagnetic Activity Indices, and Large-Scale Ambulatory ECG Databases: Toward Synchronized Environmental–Physiological Research

Emi Yuda, Junichiro Hayano

Schumann resonance (SR) is a global electromagnetic phenomenon generated within the Earth–ionosphere cavity, with a fundamental frequency near 7.83 Hz. Whether SR dynamics are associated with human autonomic nervous system activity as indexed by heart rate variability (HRV) remains an open empirical question. Addressing this question rigorously requires (a) large-scale, time-synchronized physiological datasets, (b) continuous SR monitoring with precise timestamps, and (c) control for geomagnetic activity (Kp index) as a major confounding variable. Currently available open datasets do not yet satisfy condition (a). Rather than forcing premature physiological conclusions, this paper presents a feasibility and methodology study that characterizes each data source, demonstrates the analysis pipeline on representative data, and identifies the design requirements for a future definitive study. Frequency-domain HRV indices were characterized using approximately 730,000 24-h Holter ECG recordings from the Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR). Broadband voltage SD (segment-wise standard deviation of AC-coupled voltage, used solely as a descriptive amplitude proxy) was characterized using the publicly available Mendeley Schumann Resonance Dataset (35 min segments, ~525,000 samples each). A partial correlation framework that controls for the NOAA Kp geomagnetic index is proposed and evaluated through simulation on representative parameter sets. Because the HRV and SR datasets were not collected at the same time or location, no direct physiological inference is drawn from their comparison. The reported association statistics are illustrative demonstrations of the pipeline applied to representative data, not empirical evidence of SR–HRV coupling. These findings define the data requirements and analytical methodology for future synchronized SR–HRV–Kp studies.

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