ArticleBioengineering (Basel, Switzerland)2022
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026Article
- Dynamic beat-to-beat blood pressure estimation using a multi-modal wearable deep learning approach.Physiological measurement · 2026Article
- Development and Validation of the AI-HeartAge Model in Framingham and UK Biobank.Hypertension (Dallas, Tex. : 1979) · 2026Article
- A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2026Article
- ArterialNet: Reconstructing Arterial Blood Pressure Waveform With Wearable Pulsatile Signals, a Cohort-Aware Approach.IEEE open journal of engineering in medicine and biology · 2026Article
- A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling.Sensors (Basel, Switzerland) · 2025Article
- Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring.Cureus · 2025Review
- An Ensemble-Based AI Approach for Continuous Blood Pressure Estimation in Health Monitoring Applications.Sensors (Basel, Switzerland) · 2025Article
- Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.Scientific reports · 2025Article
- A Two-Branch ResNet-BiLSTM Deep Learning Framework for Extracting Multimodal Features Applied to PPG-Based Cuffless Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2025Article
- Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure.Bioengineering (Basel, Switzerland) · 2025Article
- Development of an Improved Stacked U-Net Model for Cuffless Blood Pressure Estimation Based on PPG Signals.IEEE open journal of engineering in medicine and biology · 2025Article
- Robust modelling of arterial blood pressure reconstruction from photoplethysmography.Scientific reports · 2024Article
- MIMIC-BP: A curated dataset for blood pressure estimation.Scientific data · 2024Article
- Novel metrics for tracking blood pressure changes incontinuous cuffless blood pressure estimations.Scientific reports · 2024Article
- Examining the challenges of blood pressure estimation via photoplethysmogram.Scientific reports · 2024Article
- Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment.Communications medicine · 2024Review
- A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.Sensors (Basel, Switzerland) · 2024Article
- Fair non-contact blood pressure estimation using imaging photoplethysmography.Biomedical optics express · 2024Article
- PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.
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Registered trials
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.