ArticleBiosensors2018
Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification.
Article in Biosensors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 165 citations in OpenAlex.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Automated Detection of Hypertension Using Physiological Signals: A Review.International journal of environmental research and public health · 2021Pooled it
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- Optical methods for cuffless blood pressure measurements.Biophotonics discovery · 2026Review
- Speckle contrast optical spectroscopy for cuffless blood pressure estimation based on microvascular blood flow and volume oscillations.Biomedical optics express · 2025Article
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- Machine learning based, subject-specific, gender and race independent, non-invasive estimation of the arterial blood pressure.NPJ cardiovascular health · 2025Article
- The quest for blood pressure markers in photoplethysmography and its applications in digital health.Frontiers in digital health · 2025Article
- Optimal facial regions for remote heart rate measurement during physical and cognitive activities.NPJ cardiovascular health · 2024Article
- Article
- Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan.BMJ health & care informatics · 2024Article
- Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors.JMIR mHealth and uHealth · 2024Article
- SleepBP-Net: A Time-Distributed Convolutional Network for Nocturnal Blood Pressure Estimation from Photoplethysmogram.IEEE sensors journal · 2024Article
- Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering.Scientific reports · 2024Article
- Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter.Heliyon · 2024Article
- Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration.Bioengineering (Basel, Switzerland) · 2023Article
- Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.Biomedical engineering letters · 2023Article
- Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram.Journal of biomedical physics & engineering · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.
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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.