Year: 2024
Type: conference-paper
Abstract: Wearable devices like photoplethysmogram (PPG) sensors are prone to motion artifacts, affecting the quality of cardiovascular data.Traditional denoising methods often degrade the signal, while AI-driven solutions like deep learning (DL) struggle with random and systematic distortions, requiring large datasets for training.To overcome these limitations, this study proposes a style transfer-assisted cycle-consistent generative adversarial network (stccGAN) for denoising 3-channel PPG signals (red, green, and infrared) acquired by the patented Soundi chest sensor.Two identical devices were used: one to collect the chest PPG signal (to be denoised) and another to obtain a synchronized finger PPG signal (reference signal).The proposed stccGAN uses style transfer with dual generators featuring U-Net, GRU, and LSTM layers to improve chest PPG quality.Validation with data from 30 subjects (20 for training, 10 for testing) showed an average 70% correlation with the reference signal, with a 15% improvement over raw chest PPG.This demonstrates effective signal restoration, enabling accurate cardiac assessment and blood pressure estimation from chest PPG signals.
Source: Computing in cardiology
Institutions: Politecnico di Milano, University of Pavia
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405435065