Design and Implementation of Low-Cost Non-Invasive Blood Pressure Measurement Device from Photoplethysmography with Signal Quality Assessment

Abstract

Cardiovascular diseases remain a leading global health concern, necessitating accurate, non-invasive blood pressure monitoring solutions. This study presents a novel photoplethysmography (PPG)-based system that integrates three key innovations: (1) a real-time signal quality assessment (SQA) model employing a one-class SVM classifier to filter unreliable PPG segments, (2) a computationally efficient two-stage neural network (1D U-Net followed by 1D MultiResUNet) for arterial blood pressure (ABP) waveform estimation, and (3) a wearable hardware platform combining a MAX30102 optical sensor and ESP32 microcontroller for portable deployment. The SQA model uses five optimized features to achieve robust motion artifact rejection. The deep learning pipeline reconstructs ABP waveforms with mean absolute errors of 4.7 mmHg (systolic) and 4.3 mmHg (diastolic), complying with the Association for the Advancement of Medical Instrumentation (AAMI) standards. Validated on 40 subjects against reference sphygmomanometer measurements, the system demonstrates 92.5% (systolic) and 95% (diastolic) classification accuracy, with subsecond latency and low-power operation. By addressing critical challenges in motion robustness, computational efficiency, and clinical validation, this work advances the practicality of cuffless BP monitoring for telehealth and resource-limited settings.