ArticleSensors (Basel, Switzerland)2020
End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- Enhancing diagnostic reliability in non-invasive health Monitoring: An analytical framework for optimizing magnetic sensor-skin interactions in biomedical applications.Materials today. Bio · 2025Review
- Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.Nature reviews. Cardiology · 2025Review
- Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models.Journal of medical systems · 2025Article
- UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions.Scientific reports · 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
- A finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis.Biomedical engineering online · 2025Article
- A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- A review of machine learning methods for non-invasive blood pressure estimation.Journal of clinical monitoring and computing · 2025Review
- Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.Sensors (Basel, Switzerland) · 2024Article
- Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals.Scientific reports · 2024Article
- DAFT-Net: Dual Attention and Fast Tongue Contour Extraction Using Enhanced U-Net Architecture.Entropy (Basel, Switzerland) · 2024Article
- Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Hypothesis Scoring for Confidence-Aware Blood Pressure Estimation With Particle Filters.IEEE journal of biomedical and health informatics · 2023Article
- Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research.Sensors (Basel, Switzerland) · 2023Article
- Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation.NPJ digital medicine · 2023Article
- Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.NPJ digital medicine · 2023Review
- Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor.Biomedical engineering letters · 2023Article
- [A non-contact continuous blood pressure measurement method based on video stream].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.
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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.