Generative adversarial networks with fully connected layers to denoise PPG signals

The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.

A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.

The heart rate of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error (MAE) of 1.31 BPM comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 BPM.

The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of heart rates (70-115 BPM), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.