Dynalets: A New Representation of Periodic Biological Signals and Spectral Data

The biological information coming from electro-physiologic signal sensors like ECG or molecular signal devices like mass spectrometry has to be compressed for an efficient medical use by clinicians or to retain only the pertinent explanatory information about the mechanisms at the origin of the recorded signal for the researchers in life sciences. When the signal is periodic in time and/or space, classical compression processes like Fourier and wavelets transforms give good results concerning the compression rate, but bring in general no supplementary information about the interactions between elements of the living system producing the studied signal. Here, we define a new transform called dynalet based on Liénard differential equations susceptible to model the mechanism that is the source of the signal and we propose to apply this new technique to real signals like ECG, pulse activity and protein spectra in mass spectrometry.