Methods and devices for long term, cuffless, and continuous arterial blood pressure estimation using an end-to-end deep learning approach are provided. A deep learning system comprises three modules: a deep convolutional neural network (CNN) module for learning powerful features； a one- or multi-layer recurrent neural network (RNN) module for modeling the temporal dependencies in blood pressure dynamics； and a mixture density network (MDN) module for predicting final blood pressure value. This system takes raw physiological signals, such as photoplethysmogram and/or electrocardiography signals, as inputs and yields arterial blood pressure readings in real time.
