Predicting Blood Pressure with Deep Bidirectional LSTM Network

Blood pressure (BP) has been a difficult vascular risk factor to measure continuously and precisely with a small cuffless electronic gadget. In the meantime, it is the key biomarker for control of cardiovascular diseases (CVD), the leading cause of death worldwide. In this work, we addressed the current limitation of BP prediction models by formulating BP extraction as a temporal sequence prediction problem in which both the input and target are sequential data. By incorporating both a bidirectional layer structure and a deep architecture in a standard long short term-memory (LSTM), we established a deep bidirectional LSTM (DB-LSTM) network that can adaptively discover the latent structures of different timescales in BP sequences and automatically learn such multiscale dependencies. We evaluated our proposed model on a one-day and four-day continuous BP dataset, and the results show that DB-LSTM network can effectively learn different timescale dependencies in the BP sequences and advances the state-of-the-art by achieving superior accuracy performance than other leading methods on both datasets. To the best of our knowledge, this is the first study to validate the ability of recurrent neural networks to learn the different timescale dependencies of long-term continuous BP sequence.