ArticleBiosensors2021
Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Personalized Calibration and Hybrid Feature Fusion for Continuous Blood Pressure Estimation Using PPG and ECG Signals.Sensors (Basel, Switzerland) · 2026Article
- Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives.Physical and engineering sciences in medicine · 2025Article
- 1DCNN-BiLSTM-transformer hypertension risk prediction model based on APW.Frontiers in microbiology · 2025Article
- The quest for blood pressure markers in photoplethysmography and its applications in digital health.Frontiers in digital health · 2025Article
- 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
- 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
- Automated myocardial infarction and angina detection using second derivative of photoplethysmography.Physical and engineering sciences in medicine · 2023Article
- Article
- Early detection of myocardial ischemia in resting ECG: analysis by HHT.Biomedical engineering online · 2023Article
- Blood pressure stratification using photoplethysmography and light gradient boosting machine.Frontiers in physiology · 2023Article
- Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Research on recognition and classification of pulse signal features based on EPNCC.Scientific reports · 2022Article
- Photonic Biosensors: Detection, Analysis and Medical Diagnostics.Biosensors · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient's blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert-Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG's first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.
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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.