  Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a
promising candidate for modern BP measurements, as PPG signals can be easily
obtained from wearable devices in a non-invasive manner, allowing quick BP
measurement. However, the performance of existing machine learning-based BP
measuring methods still fall behind some BP measurement guidelines and most of
them provide only point estimates of systolic blood pressure (SBP) and
diastolic blood pressure (DBP). In this paper, we present a cutting-edge method
which is capable of continuously monitoring BP from the PPG signal and
satisfies healthcare criteria such as the Association for the Advancement of
Medical Instrumentation (AAMI) and the British Hypertension Society (BHS)
standards. Furthermore, the proposed method provides the reliability of the
predicted BP by estimating its uncertainty to help diagnose medical condition
based on the model prediction. Experiments on the MIMIC II database verify the
state-of-the-art performance of the proposed method under several metrics and
its ability to accurately represent uncertainty in prediction.
