Embedded biomedical development system for photoplethysmography signal processing based on a Programmable System-on-Chip (PSoC)

Abstract

This work presents the design and implementation of an embedded platform that acquires and processes 
photoplethysmography (PPG) signals using only the internal resources of a programmable system-on-chip (PSoC). The acquisition unit incorporates LED emitters and a photodetector to capture variations in blood absorbance, followed by an analog conditioning stage designed with the PSoC’s internal operational amplifiers and a 12-bit SAR converter. The firmware implements a state machine that manages sampling at 500 Hz, while a graphical user interface (GUI) performs IIR digital filtering to reduce motion artifacts and low-frequency noise. To validate its performance, the embedded system was compared with a commercial oximeter. Under resting conditions, PPG signals were recorded 
simultaneously, and RR intervals and heart rate were calculated. The results showed a mean absolute error (MAE) of only 2.69 BPM compared to the reference oximeter. The use of integrated operational amplifiers and reconfigurable peripherals allowed for optimized power consumption and a reduced board size without compromising signal quality. The inherent flexibility of the PSoC architecture enables the implementation of a wide range of filtering and peak detection algorithms tailored to specific device requirements. This highlights the advantages of integrating analog and digital functionalities within a single chip, as opposed to conventional microcontroller-based solutions relying on 
discrete components. In the present study, the analog OpAmp module from the PSoC 4200M family (CY8C4247AZI-M485) is employed for preprocessing and conditioning of the PPG signal. 
In conclusion, the PSoC-based prototype delivered satisfactory results in terms of accuracy, energy efficiency, and miniaturization, positioning itself as a promising alternative for telemedicine applications and continuous heart rate monitoring. Moreover, it offers a pathway for expanding toward advanced heart rate variability analysis and additional biomedical signal processing capabilities.