  Mean aortic pressure (MAP) is a major determinant of perfusion in all organs
systems. The ability to forecast MAP would enhance the ability of physicians to
estimate prognosis of the patient and assist in early detection of hemodynamic
instability. However, forecasting MAP is challenging because the blood pressure
(BP) time series is noisy and can be highly non-stationary. The aim of this
study was to forecast the mean aortic pressure five minutes in advance, using
the 25 Hz time series data of previous five minutes as input. We provide a
benchmark study of different deep learning models for BP forecasting. We
investigate a left ventricular dwelling transvalvular micro-axial device, the
Impella, in patients undergoing high-risk percutaneous intervention. The
Impella provides hemodynamic support, thus aiding in native heart function
recovery. It is also equipped with pressure sensors to capture high frequency
MAP measurements at origin, instead of peripherally. Our dataset and the
clinical application is novel in the BP forecasting field. We performed a
comprehensive study on time series with increasing, decreasing, and stationary
trends. The experiments show that recurrent neural networks with Legendre
Memory Unit achieve the best performance with an overall forecasting error of
1.8 mmHg.
