A Bayesian Residual Transform for Signal Processing

Multiscale decomposition has been an invaluable tool for the processing of physiological signals. Much focus on multiscale decomposition for processing such signals have been based on scale-space theory and wavelet transforms. In this paper, we take a different perspective on multiscale decomposition by investigating the feasibility of utilizing a Bayesian-based method for multiscale signal decomposition called Bayesian residual transform (BRT) for the purpose of physiological signal processing. In BRT, a signal is modeled as the summation of residual signals, each characterizing information from the signal at different scales. A deep cascading framework is introduced as a realization of the BRT. Signal-to-noise ratio analysis using electrocardiography signals was used to illustrate the feasibility of using the BRT for suppressing the noise in physiological signals. Results in this paper show that it is feasible to utilize the BRT for processing physiological signals for tasks, such as noise suppression.

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