Multiscale characterization of chronobiological signals based on the discrete wavelet transform

To compensate for the deficiency of conventional frequency-domain or time-d omain analysis, this paper presents a multiscale approach to characterize t he chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of t he wavelet coefficients at different scales give a complete characterizatio n of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter prop erty of the DWT in the frequency domain, we also characterized the band-rel ated activities by calculating energy in respective rhythmic bands. Moreove r, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficie nt and complete study of the temporal-frequency dynamics of the CTS, Prelim inary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS an alysis.