  Objective: The paper focuses on development of robust and accurate processing
solutions for continuous and cuff-less blood pressure (BP) monitoring. In this
regard, a robust deep learning-based framework is proposed for computation of
low latency, continuous, and calibration-free upper and lower bounds on the
systolic and diastolic BP. Method: Referred to as the BP-Net, the proposed
framework is a novel convolutional architecture that provides longer effective
memory while achieving superior performance due to incorporation of casual
dialated convolutions and residual connections. To utilize the real potential
of deep learning in extraction of intrinsic features (deep features) and
enhance the long-term robustness, the BP-Net uses raw Electrocardiograph (ECG)
and Photoplethysmograph (PPG) signals without extraction of any form of
hand-crafted features as it is common in existing solutions. Results: By
capitalizing on the fact that datasets used in recent literature are not
unified and properly defined, a benchmark dataset is constructed from the
MIMIC-I and MIMIC-III databases obtained from PhysioNet. The proposed BP-Net is
evaluated based on this benchmark dataset demonstrating promising performance
and shows superior generalizable capacity. Conclusion: The proposed BP-Net
architecture is more accurate than canonical recurrent networks and enhances
the long-term robustness of the BP estimation task. Significance: The proposed
BP-Net architecture addresses key drawbacks of existing BP estimation
solutions, i.e., relying heavily on extraction of hand-crafted features, such
as pulse arrival time (PAT), and; Lack of robustness. Finally, the constructed
BP-Net dataset provides a unified base for evaluation and comparison of deep
learning-based BP estimation algorithms.
