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Biosignals are nowadays important subjects for scientific researches fromboth theory and applications especially with the appearance of new pandemicsthreatening humanity such as the new Coronavirus. One aim in the present workis to prove that Wavelets may be successful machinery to understand suchphenomena by applying a step forward extension of wavelets to multiwavelets. Weproposed in a first step to improve the multiwavelet notion by constructingmore general families using independent components for multi-scaling andmultiwavelet mother functions. A special multiwavelet is then introduced,continuous and discrete multiwavelet transforms are associated, as well as newfilters and algorithms of decomposition and reconstruction. The constructedmultiwavelet framework is applied for some experimentations showing fastalgorithms, ECG signal, and a strain of Coronavirus processing.

Towards New Multiwavelets: Associated Filters and Algorithms. Part I:  Theoretical Framework and Investigation of Biomedical Signals, ECG and  Coronavirus Cases