  Cardiovascular diseases are one of the most severe causes of mortality,
taking a heavy toll of lives annually throughout the world. The continuous
monitoring of blood pressure seems to be the most viable option, but this
demands an invasive process, bringing about several layers of complexities.
This motivates us to develop a method to predict the continuous arterial blood
pressure (ABP) waveform through a non-invasive approach using
photoplethysmogram (PPG) signals. In addition we explore the advantage of deep
learning as it would free us from sticking to ideally shaped PPG signals only,
by making handcrafted feature computation irrelevant, which is a shortcoming of
the existing approaches. Thus, we present, PPG2ABP, a deep learning based
method, that manages to predict the continuous ABP waveform from the input PPG
signal, with a mean absolute error of 4.604 mmHg, preserving the shape,
magnitude and phase in unison. However, the more astounding success of PPG2ABP
turns out to be that the computed values of DBP, MAP and SBP from the predicted
ABP waveform outperforms the existing works under several metrics, despite that
PPG2ABP is not explicitly trained to do so.
