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Cardiovascular diseases are one of the most severe causes of mortality,taking a heavy toll of lives annually throughout the world. The continuousmonitoring of blood pressure seems to be the most viable option, but thisdemands an invasive process, bringing about several layers of complexities.This motivates us to develop a method to predict the continuous arterial bloodpressure (ABP) waveform through a non-invasive approach usingphotoplethysmogram (PPG) signals. In addition we explore the advantage of deeplearning as it would free us from sticking to ideally shaped PPG signals only,by making handcrafted feature computation irrelevant, which is a shortcoming ofthe existing approaches. Thus, we present, PPG2ABP, a deep learning basedmethod, that manages to predict the continuous ABP waveform from the input PPGsignal, with a mean absolute error of 4.604 mmHg, preserving the shape,magnitude and phase in unison. However, the more astounding success of PPG2ABPturns out to be that the computed values of DBP, MAP and SBP from the predictedABP waveform outperforms the existing works under several metrics, despite thatPPG2ABP is not explicitly trained to do so.

PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks