Aortic Pressure Forecasting with Deep Sequence Learning
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Mean aortic pressure is a major determinant of perfusion in all organ systems. The ability to forecast the mean aortic pressure would enhance the ability of physicians to estimate prognosis of the patient and assist in early detection of hemodynamic instability. However, forecasting aortic pressure is challenging because the blood pressure time series is noisy and can be highly non-stationary. In this study, we provided a benchmark study of different deep sequence learning models on pump performance data obtained in patients who underwent high-risk percutaneous intervention with transvalvular micro-axial heart pump support. The aim of this study was to forecast the mean aortic pressure five minutes in advance, using the time series data of previous five minutes as input. We performed comprehensive study on time series with increasing, decreasing, and stationary trends. The experiments show promising results with the Legendre Memory Unit architecture achieving the best performance with an overall RMSE of 1.837 mmHg.
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