EstNet: A Non-Invasive Blood Pressure Estimation System using Photoplethysmography Signal

Author(s)	Priyanshi Sharma, Gruhethi Malla
Country	India
Abstract	Non-invasive, continuous blood pressure (BP) monitoring is crucial for early detection of hypertension. Photoplethysmography (PPG) signals offer a low-cost, wearable-compatible alternative to cuff-based measurements. This study proposes EstNet, a hybrid CNN–GRU model for direct systolic and diastolic BP estimation from PPG signals from the MIMIC-III Waveform Database. After structured pre-processing, EstNet outperformed eleven machine learning and deep learning models, achieving SBP MAE=7.28 mmHg and DBP MAE=5.01 mmHg, meeting AAMI standards and BHS Grade A, demonstrating clinical reliability for cuffless BP monitoring.
Keywords	Photoplethysmography, Blood Pressure Estimation, Deep Learning, Cuffless Monitoring, MIMIC III Dataset.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-22
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61453
Short DOI	https://doi.org/hbb76r

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