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

Zheng Duanmu¹, Haojie Gong¹, Siyuan Lv¹

¹School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192, Beijing, China.

Annals of Biomedical Engineering

|July 7, 2025

Summary

A novel BiSTU Sequential Network accurately predicts arterial blood pressure (ABP) waveforms, aiding cardiovascular disease (CVD) diagnosis. This AI model shows significant potential for non-invasive ABP monitoring and early CVD detection.

Keywords:

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

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Cardiovascular Physiology

Background:

Cardiovascular disease (CVD) is a major global health concern.
Arterial blood pressure (ABP) waveform analysis is crucial for early CVD diagnosis.
Current methods for ABP waveform evaluation lack sufficient accuracy.

Purpose of the Study:

To propose a novel U-net joint network architecture, the BiSTU Sequential Network.
To develop a model capable of predicting high-quality arterial blood pressure waveforms.
To improve the accuracy of non-invasive ABP prediction for early CVD detection.

Main Methods:

Integration of Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies.
Inclusion of a Transformer model with multi-head attention for detailed feature extraction.
Utilizing a MultiRes Convolutional Block Attention Module U-Net (MCBAMU-Net) for multi-scale feature extraction.
Training the model on 12,000 vital sign records from 942 ICU patients.

Main Results:

Predicted ABP waveforms closely matched actual waveforms (R-squared of 0.98).
Achieved a Mean Absolute Error (MAE) of 1.78 ± 2.15 mmHg and Root Mean Square Error (RMSE) of 2.79 mmHg.
Met Association for the Advancement of Medical Instrumentation (AAMI) standards for SBP and DBP.
Exceeded British Hypertension Society (BHS) standards for accuracy within 5 mmHg and 15 mmHg.

Conclusions:

The BiSTU Sequential Network demonstrates significant potential for accurate, non-invasive ABP prediction.
Model predictions align with clinical standards, indicating broad application prospects.
Contributes to the early diagnosis and monitoring of cardiovascular diseases.