ArticleJournal of circadian rhythms2013
Wavelet analysis of circadian and ultradian behavioral rhythms.
Article in Journal of circadian rhythms, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
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- Recognition of distinct sleep states inbioRxiv : the preprint server for biology · 2025Article
- Self-sustained rhythmic behavior ofPNAS nexus · 2025Article
- Augmenting Circadian Biology Research With Data Science.Journal of biological rhythms · 2025Review
- Evaluation of the Digital Ventilated Cage® system for circadian phenotyping.Scientific reports · 2025Article
- Ultradian Rest-activity Rhythms Induced by Quinpirole in Mice Using Wavelet Analysis.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Emergent collective behavior evolves more rapidly than individual behavior among acorn ant species.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep learning model using continuous skin temperature data predicts labor onset.BMC pregnancy and childbirth · 2024Article
- Ultradian rhythms of activity in a wild subterranean rodent.Biology letters · 2024Article
- Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping.Nature communications · 2024Article
- Silencing of ultradian rhythms and metabolic depression during spontaneous daily torpor in Djungarian hamsters.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2024Article
- Multiple ultradian rhythms of metabolism, body temperature and activity in Djungarian hamsters.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2024Article
- Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting.Journal of biological rhythms · 2024Article
- Rhythmidia: A modern tool for circadian period analysis of filamentous fungi.PLoS computational biology · 2024Article
- Article
- Approaches for assessing circadian rest-activity patterns using actigraphy in cohort and population-based studies.Current sleep medicine reports · 2023Article
- Synchronized locomotion can improve spatial accessibility inside ant colonies.Proceedings. Biological sciences · 2023Article
- Parametric effects of light acting via multiple photoreceptors contribute to circadian entrainment inProceedings. Biological sciences · 2023Article
- Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling.Cell reports methods · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
: We review time-frequency methods that can be useful in quantifying circadian and ultradian patterns in behavioral records. These records typically exhibit details that may not be captured through commonly used measures such as activity onset and so may require alternative approaches. For instance, activity may involve multiple bouts that vary in duration and magnitude within a day, or may exhibit day-to-day changes in period and in ultradian activity patterns. The discrete Fourier transform and other types of periodograms can estimate the period of a circadian rhythm, but we show that they can fail to correctly assess ultradian periods. In addition, such methods cannot detect changes in the period over time. Time-frequency methods that can localize frequency estimates in time are more appropriate for analysis of ultradian periods and of fluctuations in the period. The continuous wavelet transform offers a method for determining instantaneous frequency with good resolution in both time and frequency, capable of detecting changes in circadian period over the course of several days and in ultradian period within a given day. The discrete wavelet transform decomposes a time series into components associated with distinct frequency bands, thereby facilitating the removal of noise and trend or the isolation of a particular frequency band of interest. To demonstrate the wavelet-based analysis, we apply the transforms to a numerically-generated example and also to a variety of hamster behavioral records. When used appropriately, wavelet transforms can reveal patterns that are not easily extracted using other methods of analysis in common use, but they must be applied and interpreted with care.
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Registered trials
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