Adaptive, artificial intelligence-based system for processing photoplethysmography (PPG) signals for personalized health monitoring

Abstract

A system for explainable and adaptive processing of photoplethysmography signals using artificial intelligence for personalized health monitoring, comprising: - a physiological sensor unit consisting of at least one optical emitter and at least one photodetector configured to acquire photoplethysmography signals from biological tissue; - an analog processing unit electrically connected to the physiological sensor unit and configured to amplify, filter, and digitize acquired photoplethysmography signals; - a signal quality assessment processor configured to determine signal integrity features such as motion distortion, baseline deviations, optical background noise, pulse irregularities, and waveform discontinuities associated with the digitized photoplethysmography signals;- an adaptive preprocessing processor that is communicatively linked to the signal quality assessment processor and configured to dynamically modify the signal conditioning parameters based on determined signal integrity features, with the adaptive preprocessing processor performing frequency-selective filtering, motion artifact suppression, waveform normalization, and cardiac cycle segmentation; - a physiological representation processor configured to extract physiologically derived parameters such as heart rate variability, pulse waveform morphology, vascular compliance indicators, pulse interval measurements, and waveform reflection properties from segmented cardiac cycles; - a machine learning processor configured to generate data-driven feature representations from the segmented cardiac cycles using temporal sequence analysis and convolutional waveform analysis;- a hybrid feature integration processor configured to combine physiology-derived parameters and data-driven feature representations into a unified multidimensional physiological representation structure; a context inference processor configured to integrate the unified multidimensional physiological representation structure with contextual data including user activity conditions, temporal physiological patterns, environmental conditions, and individual baseline features to generate personalized health-related outputs; - a personalization processor configured to continuously update preprocessing parameters, feature weighting parameters, and inference thresholds using longitudinal physiological observations;and a communication unit configured to transmit generated health-related outputs to at least one external monitoring device, healthcare interface, or remote computing infrastructure.