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Objective: The paper focuses on development of robust and accurate processingsolutions for continuous and cuff-less blood pressure (BP) monitoring. In thisregard, a robust deep learning-based framework is proposed for computation oflow latency, continuous, and calibration-free upper and lower bounds on thesystolic and diastolic BP. Method: Referred to as the BP-Net, the proposedframework is a novel convolutional architecture that provides longer effectivememory while achieving superior performance due to incorporation of casualdialated convolutions and residual connections. To utilize the real potentialof deep learning in extraction of intrinsic features (deep features) andenhance the long-term robustness, the BP-Net uses raw Electrocardiograph (ECG)and Photoplethysmograph (PPG) signals without extraction of any form ofhand-crafted features as it is common in existing solutions. Results: Bycapitalizing on the fact that datasets used in recent literature are notunified and properly defined, a benchmark dataset is constructed from theMIMIC-I and MIMIC-III databases obtained from PhysioNet. The proposed BP-Net isevaluated based on this benchmark dataset demonstrating promising performanceand shows superior generalizable capacity. Conclusion: The proposed BP-Netarchitecture is more accurate than canonical recurrent networks and enhancesthe long-term robustness of the BP estimation task. Significance: The proposedBP-Net architecture addresses key drawbacks of existing BP estimationsolutions, i.e., relying heavily on extraction of hand-crafted features, suchas pulse arrival time (PAT), and; Lack of robustness. Finally, the constructedBP-Net dataset provides a unified base for evaluation and comparison of deeplearning-based BP estimation algorithms.

BP-Net: Cuff-less, Calibration-free, and Non-invasive Blood Pressure Estimation via a Generic Deep Convolutional Architecture