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Cardiovascular diseases are the most common causes of death around the world.To detect and treat heart-related diseases, continuous Blood Pressure (BP)monitoring along with many other parameters are required. Several invasive andnon-invasive methods have been developed for this purpose. Most existingmethods used in the hospitals for continuous monitoring of BP are invasive. Onthe contrary, cuff-based BP monitoring methods, which can predict SystolicBlood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used forcontinuous monitoring. Several studies attempted to predict BP fromnon-invasively collectible signals such as Photoplethysmogram (PPG) andElectrocardiogram (ECG), which can be used for continuous monitoring. In thisstudy, we explored the applicability of autoencoders in predicting BP from PPGand ECG signals. The investigation was carried out on 12,000 instances of 942patients of the MIMIC-II dataset and it was found that a very shallow,one-dimensional autoencoder can extract the relevant features to predict theSBP and DBP with the state-of-the-art performance on a very large dataset.Independent test set from a portion of the MIMIC-II dataset provides an MAE of2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of fortysubjects, the model trained on the MIMIC-II dataset, provides an MAE of 2.728and 1.166 for SBP and DBP, respectively. For both the cases, the results metBritish Hypertension Society (BHS) Grade A and surpassed the studies from thecurrent literature.

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals