ArticleSensors (Basel, Switzerland)2021
A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026Article
- Towards trustworthy AI-driven cuffless blood pressure monitoring.NPJ digital medicine · 2026Article
- Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.Scientific reports · 2025Article
- Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.Biomedical engineering letters · 2023Article
- Non-Invasive Blood Pressure Sensing via Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor.Sensors (Basel, Switzerland) · 2023Article
- Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet.Bioengineering (Basel, Switzerland) · 2023Article
- Advancing Digital Medicine with Wearables in the Wild.Sensors (Basel, Switzerland) · 2022Article
- Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.BioMed research international · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
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Registered trials
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