SynthesisInternational journal of environmental research and public health2021
Automated Detection of Hypertension Using Physiological Signals: A Review.
Synthesis in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 52 citations in OpenAlex.
- Review
- Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram.Sensors (Basel, Switzerland) · 2025Article
- Critical appraisal of machine learning-based hypertension detection via single-lead electrocardiograms.Journal of human hypertension · 2025Article
- Biofeedback in Pediatric, Adolescent, and Young Adult Cancer Care: A Systematic Review.Children (Basel, Switzerland) · 2025Review
- ENaC Biomarker Detection in Platelets Using a Lateral Flow Immunoassay: A Clinical Validation Study.Biosensors · 2025Article
- A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data.Sensors (Basel, Switzerland) · 2025Article
- Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.Journal of human hypertension · 2025Observational
- Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation.Heliyon · 2024Article
- Changepoint Detection in Heart Rate Variability Indices in Older Patients Without Cancer at End of Life Using Ballistocardiography Signals: Preliminary Retrospective Study.JMIR formative research · 2024Article
- A Serious Game to Self-Regulate Heart Rate Variability as a Technique to Manage Arousal Level Through Cardiorespiratory Biofeedback: Development and Pilot Evaluation Study.JMIR serious games · 2023Article
- Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review.Materials (Basel, Switzerland) · 2023Review
- Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges.Bioengineering (Basel, Switzerland) · 2022Review
- Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Exercise preconditioning improves electrocardiographic signs of myocardial ischemic/hypoxic injury and malignant arrhythmias occurring after exhaustive exercise in rats.Scientific reports · 2022Article
- Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals.International journal of environmental research and public health · 2022Article
- Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV 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 Socratic graph.