Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network
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Blood pressure (BP) has been a difficult vascular risk factor to measure precisely and continuously due to its multiscale temporal dependencies. However, both pulse transit time (PTT) model and regression model fail to learn such dependencies and thus suffer from accuracy decay over time. In this work, we addressed the limitation of existing BP prediction models by formulating BP extraction as a sequence prediction problem in which both the input and target are temporal sequence. 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 static and follow-up 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 multiscale dependencies of long-term continuous BP sequence.
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