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This letter presents a novel hybrid method that leverages deep learning toexploit the multi-resolution analysis capability of the wavelets, in order todenoise a photoplethysmography (PPG) signal. Under the proposed method, a noisyPPG sequence of length N is first decomposed into L detailed coefficients usingthe fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructedas follows. A custom feedforward neural network (FFNN) provides the binaryweights for each of the wavelet sub-signals outputted by the inverse-FWT block.This way, all those sub-signals which correspond to noise or artefacts arediscarded during reconstruction. The FFNN is trained on the Beth IsraelDeaconess Medical Center (BIDMC) dataset under the supervised learningframework, whereby we compute the mean squared-error (MSE) between the denoisedsequence and the reference clean PPG signal, and compute the gradient of theMSE for the back-propagation. Numerical results show that the proposed methodeffectively denoises the corrupted PPG and video-PPG signal.

A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for Denoising of PPG Signals