Cross-wavelet analysis allows obtaining high temporal and frequency resolution in heart rate synchrony analysis.

Overview

1. Department of Psychology, University of Konstanz, Konstanz, Germany
2. Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
3. Department of Biology, Centre for Vision Research, York University, Toronto, ON, Canada
4. Department of Child and Adolescent Psychiatry/Psychotherapy, University of Ulm, Ulm, Germany

Abstract

Interpersonal synchrony, the temporal correspondence of repeated behavioral, physiological, or neural measures between individuals, has been analyzed using a variety of different methods. However, the analysis method needs to be carefully chosen to ensure an adequate interpretation of the results. Here, we suggest using cross-wavelet power for interpersonal synchrony analysis due to several advantages. We demonstrate the proposed method using the example of heart rate synchrony. In cross-wavelet power analysis, synchrony is determined per frequency band and time point, allowing for both fine-grained frequency and temporal resolution. We argue that applying this approach to analyze synchrony in heart rate data provides additional information about underlying processes that may influence overall heart rate alignment between individuals. We describe the principles of cross-wavelet power analysis and compare the method to the frequently used cross-correlational approach using simulated and real data. Cross-correlation is a linear measure of the similarity between time series, which includes the quantification of leader-follower relationships. We illustrate different implications for which data series are considered to be synchronous and describe the advantages and drawbacks of using cross-wavelet analysis across various possible synchronization scenarios. The main advantage of cross-wavelet power is its high time- and frequency resolution, whereas cross-correlation is more suitable when researchers aim to differentiate between synchrony types. Finally, we provide recommendations for implementing cross-wavelet power analysis, including R code to facilitate the application to one’s own data.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. The code for data simulation and analysis is available under https://gitlab.inf.uni-konstanz.de/bernadette.denk/cwp_for_sync_analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 8 keywords, 1 funder, 49 references.

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This paper

Denk, B. F., Wienhold, S., Volkmer, N., Troje, N. F., Meier, M., & Pruessner, J. C. (2026). Cross-wavelet analysis allows obtaining high temporal and frequency resolution in heart rate synchrony analysis. Frontiers in network physiology, 6, 1869004. https://doi.org/10.3389/fnetp.2026.1869004

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