  Blood Pressure (BP) is one of the four primary vital signs indicating the
status of the body's vital (life-sustaining) functions. BP is difficult to
continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff),
especially in everyday-setting. However, other health signals which can be
easily and continuously acquired, such as photoplethysmography (PPG), show some
similarities with the Aortic Pressure waveform. Based on these similarities, in
recent years several methods were proposed to predict BP from the PPG signal.
Building on these results, we propose an advanced personalized data-driven
approach that uses a three-layer deep neural network to estimate BP based on
PPG signals. Different from previous work, the proposed model analyzes the PPG
signal in time-domain and automatically extracts the most critical features for
this specific application, then uses a variation of recurrent neural networks
called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP
value associated with that time window. Experimental results on two separate
standard hospital datasets, yielded absolute errors mean and absolute error
standard deviation for systolic and diastolic BP values outperforming prior
works.
