  Oscillometric monitors are the most common automated blood pressure (BP)
measurement devices used in non-specialist settings. However, their accuracy
and reliability vary under different settings and for different age groups and
health conditions. A main limitation of the existing oscillometric monitors is
their underlying analysis algorithms that are unable to fully capture the BP
information encoded in the pattern of the recorded oscillometric pulses. In
this paper, we propose a new 2D oscillometric data representation that enables
a full characterization of arterial system and empowers the application of deep
learning to extract the most informative features correlated with BP. A hybrid
convolutional-recurrent neural network was developed to capture the
oscillometric pulses morphological information as well as their temporal
evolution over the cuff deflation period from the 2D structure, and estimate
BP. The performance of the proposed method was verified on three oscillometric
databases collected from the wrist and upper arms of 245 individuals. It was
found that it achieves a mean error and a standard deviation of error of as low
as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and
2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method
outperformed the state-of-the-art techniques and satisfied the current
international standards for BP monitors by a wide margin. The proposed method
shows promise toward robust and objective BP estimation in a variety of
patients and monitoring situations.
