EstNet: A Non-Invasive Blood Pressure Estimation System Using Photoplethysmography Signals

Continuous noninvasive monitoring of blood pressure (BP) plays a crucial role in the early detection and management of hypertension and cardiovascular disorders. Photoplethysmography (PPG), widely used in wearable technologies, provides a practical and low-cost alternative to conventional cuff-based measurement systems. This study presents EstNet, a hybrid deep learning framework that integrates convolutional neural networks and gated recurrent units for the direct estimation of systolic and diastolic blood pressure from raw PPG signals. PPG data were obtained from the MIMIC-III waveform database, sampled at 120 Hz, and segmented into fixed-length windows. A structured preprocessing pipeline, including filtering, detrending, and normalization, was applied to enhance signal quality and reliability. Multiple machine learning and deep learning models were evaluated for comparison, and the proposed convolutional neural network-gated recurrent unit (CNN-GRU) model demonstrated the best overall performance. The model achieved a mean absolute error of 7.28 mmHg for systolic blood pressure and 5.01 mmHg for diastolic blood pressure using a subject-independent evaluation strategy. The findings indicate that hybrid convolutional-recurrent architectures effectively capture both morphological and temporal characteristics of PPG signals, enabling accurate cuffless blood pressure estimation. The proposed approach shows strong potential for integration into wearable systems for continuous and real-time health monitoring.