Evidence map›Paper›PMID 40622504›Full record

ArticleAnnals of biomedical engineering2025

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

Zheng Duanmu, Haojie Gong, Siyuan Lv, Wenyue Yan, Qianxi Cheng, Jinqiu Sang, Xilan Yang, Louqian Zhang

Abstract read
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In one paragraph

Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zheng DuanmuSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192, Beijing, China.ORCID http://orcid.org/0000-0002-3594-7179
Haojie GongSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192, Beijing, China.
Siyuan LvSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192, Beijing, China.
Wenyue YanDepartment of General Practice, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, Jiangsu, China.
Qianxi ChengSchool of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192, Beijing, China.
Jinqiu SangShanghai Institute of AI for Education, East China Normal University, Shanghai, 10587, Shanghai, China.
Xilan YangDepartment of General Practice, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, Jiangsu, China. xilanyang@njmu.edu.cn.
Louqian ZhangDepartment of Thoracic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210009, Jiangsu, China. zhanglouqian@njmu.edu.cn.ORCID http://orcid.org/0000-0002-6809-3915

Funding

National Key R&D Program of China 2021YFB3201705National Natural Science Foundation of China 82300514Science and Technology Develop ment Fund Project of Nanjing Health Planning Commission YKK23070Science and Technology Develop ment Fund Project of Nanjing Health Planning Commission YKK23207
6 · The paper itself

Abstract

purposeCardiovascular 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.

methodsThe 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.

resultsExperimental 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

conclusionThe 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.

Indexed as

Blood PressureCardiovascular DiseasesNeural Networks, ComputerPhotoplethysmographySignal Processing, Computer-AssistedFemaleHumansMaleMiddle AgedDeep learning blood pressure curveNon-invasive blood pressure measurement deep supervisionPulse waveTransformer modelU-net

Identifiers

PMID40622504

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.