Enhanced Noise Reduction in Photoplethysmography Signals Using a Denoising Autoencoder

CNN), deep learningWe propose a dilated denoising autoencoder (DDAE) based on a multilayer one-dimensional convolutional neural network (CNN) to remove motion-induced noise-baseline wander (BW), muscle artifacts (MA), and electrode motion (EM)-from photoplethysmography (PPG) signals in the PPG-DaLiA dataset acquired using the Empatica E4 wrist-worn wearable device.The model integrates dilated convolutions, residual blocks, batch normalization, Leaky ReLU, maxpooling, and skip connections to capture long-range dependences while preserving pulse morphology.Compared with baseline methods such as the deep neural network (DNN), CNN, fully convolutional network (FCN), and convolutional denoising autoencoder (CDA), it offers three advantages: (1) pooling layers and skip connections preserve key features, (2) optimized parameter design reduces computational complexity and improves efficiency, and (3) a lightweight architecture enables efficient signal reconstruction on resource-constrained hardware.Evaluated across signal-to-noise ratio (SNR) conditions (-6 to 24 dB), the model significantly improves SNR, achieving 36.27dB at -6 dB [root mean square error (RMSE): 0.008271; percentage root-mean-square difference (PRD): 13.97%] and 40.40 dB at 24 dB (RMSE: 0.005090; PRD: 9.00%.Deployed on a mobile device for real-time PPG processing, the model demonstrates superior performance and strong potential for medical and personal health applications.