Multi-Stage BiSTU Network Combining BiLSTM and Transformer for ABP Waveform Prediction from PPG Signals

Abstract

Purpose: Cardiovascular disease (CVD) remains a global health issue, and arterial blood pressure (ABP) waveforms provide critical physiological data that aid in the early diagnosis of CVD. However, existing pulse waveform evaluation methods are insufficient for accurately predicting ABP. This study aims to propose a novel U-net joint network architecture, the BiSTU Sequential Network, to predict high-quality ABP waveforms.

Methods: The designed BiSTU Sequential Network integrates a Bidirectional Long Short-Term Memory (Bi-LSTM) model to capture temporal dependencies, a Transformer model with multi-head attention mechanisms to extract detailed features, and a MultiRes Convolutional Block Attention Module U-Net (MCBAMU-Net) for multi-scale feature extraction. The model was trained using 12,000 vital sign records from 942 ICU patients.

Results: Experimental results demonstrate that the predicted ABP waveforms closely align with the actual waveforms, achieving a mean absolute error (MAE) of 1.78 ± 2.15 mmHg, a root mean square error (RMSE) of 2.79 mmHg, and an R-squared (R $_{}^2$ ) of 0.98. The model meets the standards of the Association for the Advancement of Medical Instrumentation (AAMI), with MAEs of 2.94 ± 3.43 mmHg for systolic blood pressure (SBP) and 4.22 ± 5.18 mmHg for diastolic blood pressure (DBP). Under the British Hypertension Society (BHS) standards, the accuracy rates within 5 mmHg are 85.3% for DBP and 72.4% for SBP and exceed 97% within 15 mmHg.

Conclusion: The BiSTU Sequential Network exhibits significant potential for accurate, non-invasive prediction of arterial blood pressure. Its predictions closely match actual waveforms and comply with multiple clinical standards, indicating broad application prospects and contributing to the early diagnosis and monitoring of cardiovascular diseases.

Keywords: Deep learning blood pressure curve; Non-invasive blood pressure measurement deep supervision; Pulse wave; Transformer model; U-net.