ArticleSensors (Basel, Switzerland)2023
Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- PPG-Net 4: Deep-Learning-Based Approach for Classification of Blood Flow Using Non-Invasive Dual Photoplethysmography (PPG) Signals.Sensors (Basel, Switzerland) · 2025Article
- A review of machine learning methods for non-invasive blood pressure estimation.Journal of clinical monitoring and computing · 2025Review
- Video-based estimation of blood pressure.PloS one · 2025Article
- Application of Real-Time Palm Imaging with Nelder-Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection.Biomimetics (Basel, Switzerland) · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter.Heliyon · 2024Article
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
- State-dependent facial pulsation asymmetry and phase asynchrony measured by imaging photoplethysmography and their coupling with contingent negative variation in migraine.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG signal acquisition system is designed. The system can perform experimental acquisition under ambient light, effectively reducing the cost of non-contact pulse wave signal acquisition while simplifying the operation process. The first open-source dataset IPPG-BP for IPPG signal and blood pressure data is constructed by this system, and a multi-stage blood pressure estimation model combining a convolutional neural network and bidirectional gated recurrent neural network is designed. The results of the model conform to both BHS and AAMI international standards. Compared with other blood pressure estimation methods, the multi-stage model automatically extracts features through a deep learning network and combines different morphological features of diastolic and systolic waveforms, which reduces the workload while improving accuracy.
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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.