Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

TL;DR

This paper introduces a deep recurrent neural network with LSTM, bidirectional structure, and residual connections for long-term blood pressure prediction, significantly improving accuracy over previous models.

Contribution

The paper proposes a novel deep RNN architecture with bidirectional and residual connections for sequence-based BP estimation, enhancing long-term prediction accuracy.

Findings

Achieved RMSE of 3.90 mmHg for systolic BP on static dataset.

Outperformed traditional models on multi-day BP datasets.

Significantly improved long-term BP prediction accuracy.

Abstract

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.