AI-Based Multi-Wavelength PPG Device for Blood Pressure Monitoring

Non-invasive vital signs monitoring, particularly blood pressure, plays a pivotal role in assessing overall health and detecting early signs of medical conditions. Photoplethysmogra-phy (PPG) is a non-invasive technology that is increasingly used in vital signs monitoring, carrying the advantage of continuously and accurately measuring a cardiac signal, relying on the sensitivity to blood volume changes in blood vessels, which allows the real-time recording of each heartbeat and the assessment of the presence of any abnormalities. This study explores the efficacy of machine learning models for arterial pressure prediction using the only photoplethysmography (PPG) signal, relying on a new dataset and multi-wavelength sensor technology, which provides signals derived from four different wavelengths (infrared, red, green and blue). The recruited population is made up of 88 people, performing a measurement with a duration of about 30 seconds (sampled at 100 Hz). After accurate signal pre-processing, three approaches are evaluated: a multi-layer percep-tron (MLP) leveraging 84 features per subject, a dimensionality reduction strategy using Principal Component Analysis (PCA), and a Convolutional Neural Network (CNN) architecture. The outstanding performances of CNN-based method were evaluated in terms of Mean Absolute Error (MAE) and Standard Deviation (SD), resulted in 5.52 ± 7.62 mmHg for SBP and 4.67 ± 6.63 for DBP, meeting the requirements imposed by Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS). The combined CNN and L2-norm approach demonstrated potential as a reliable tool for non-invasive arterial pressure prediction, offering valuable insights for cardiovascular health monitoring and management.