ArticleSensors (Basel, Switzerland)2020
Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network.
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 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Oscillometric blood pressure estimation using machine learning-based mapping of waveform features.Biomedical engineering letters · 2025Article
- UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions.Scientific reports · 2025Article
- Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation.Cardiovascular engineering and technology · 2024Article
- Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation.Sensors (Basel, Switzerland) · 2022Article
- Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals.IEEE open journal of engineering in medicine and biology · 2022Article
- Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?Life (Basel, Switzerland) · 2021Article
- Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Continuous blood pressure (BP) monitoring is important for patients with hypertension. However, BP measurement with a cuff may be cumbersome for the patient. To overcome this limitation, various studies have suggested cuffless BP estimation models using deep learning algorithms. A generalized model should be considered to decrease the training time, and the model reproducibility should be taken into account in multi-day scenarios. In this study, a BP estimation model with a bidirectional long short-term memory network is proposed. The features are extracted from the electrocardiogram, photoplethysmogram, and ballistocardiogram. The leave-one-subject-out (LOSO) method is incorporated to generalize the model and fine-tuning is applied. The model was evaluated using one-day and multi-day tests. The proposed model achieved a mean absolute error (MAE) of 2.56 and 2.05 mmHg for the systolic and diastolic BP (SBP and DBP), respectively, in the one-day test. Moreover, the results demonstrated that the LOSO method with fine-tuning was more compatible in the multi-day test. The MAE values of the model were 5.82 and 5.24 mmHg for the SBP and DBP, respectively.
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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.