  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 and
non-invasive methods have been developed for this purpose. Most existing
methods used in the hospitals for continuous monitoring of BP are invasive. On
the contrary, cuff-based BP monitoring methods, which can predict Systolic
Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used for
continuous monitoring. Several studies attempted to predict BP from
non-invasively collectible signals such as Photoplethysmogram (PPG) and
Electrocardiogram (ECG), which can be used for continuous monitoring. In this
study, we explored the applicability of autoencoders in predicting BP from PPG
and ECG signals. The investigation was carried out on 12,000 instances of 942
patients of the MIMIC-II dataset and it was found that a very shallow,
one-dimensional autoencoder can extract the relevant features to predict the
SBP 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 of
2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of forty
subjects, the model trained on the MIMIC-II dataset, provides an MAE of 2.728
and 1.166 for SBP and DBP, respectively. For both the cases, the results met
British Hypertension Society (BHS) Grade A and surpassed the studies from the
current literature.
