Blood Pressure Assessment from Contact Photoplethysmographic Signals Using a Combination of Deep Convolutional and Recurrent Neural Networks

High blood pressure (HBP) is at the root of many cardiovascular diseases, causing millions of deaths every year worldwide. Continuous monitoring of blood pressure (BP) is necessary, not only to prevent cardiovascular disease, but also to help those already affected. While many methods of blood pressure measurement exist at present, they present two major inconve-niences, these methods do not allow continuous measurement and they are frequently very invasive. In this paper, we present an alternative method to measure systolic and diastolic blood (SBP and DBP) pressure values with photoplethysmographic (PPG) signals using a deep learning architecture. PPG signals come from MIMIC II, a noisy and unprocessed database. Using a ResNet neural network coupled with LSTM layers, we succeeded in predicting SBP and DBP values, with an RMSE of 8.96 mmHg.