Multiscale characterization of chronobiological signals based on the discrete wavelet transform.
To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.
Chan
F H
FH
Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong. fhychan@eee.hku.hk
Wu
B M
BM
Lam
F K
FK
Poon
P W
PW
Poon
A M
AM
D000818:Animals; D002940:Circadian Rhythm; D008124:Locomotion; D008297:Male; D051379:Mice; D009043:Motor Activity; D012815:Signal Processing, Computer-Assisted