Emphasizing Difficulties in the Detection of Rhythms with Lomb-Scargle Periodograms

Abstract

The Lomb-Scargle periodogram was introduced in astrophysics to detect sinusoidal signals in noisy unevenly sampled time series. It proved to be a powerful tool in time series analysis and has recently been adapted in biomedical sciences. Its use is motivated by handling non-uniform data which is a common characteristic due to the restricted and irregular observations of, for instance, free-living animals. However, the observational data often contain fractions of non-Gaussian noise or may consist of periodic signals with non-sinusoidal shapes. These properties can make more difficult the interpretation of Lomb-Scargle periodograms and can lead to misleading estimates. In this letter we illustrate these difficulties for noise-free bimodal rhythms and sinusoidal signals with outliers. The examples are aimed to emphasize limitations and to complement the recent discussion on Lomb-Scargle periodograms.