ArticleJournal of medical systems2018
Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Evaluating the Utility of Wearable Sensors for the Early Diagnosis of Parkinson Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Automated Detection of Hypertension Using Physiological Signals: A Review.International journal of environmental research and public health · 2021Pooled it
- Hypertension screening via awake-sleep differences in photoplethysmogram signals.European heart journal. Digital health · 2026Article
- Motion-Based Confidence Score to Support the Practical Application of rPPG Methods in Health Monitoring.Journal of medical systems · 2026Article
- Article
- A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases.Healthcare (Basel, Switzerland) · 2023Review
- Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension.Sensors (Basel, Switzerland) · 2022Article
- Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review.Sensors (Basel, Switzerland) · 2022Review
- Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet.American journal of physiology. Heart and circulatory physiology · 2022Review
- Acupuncture for hypertension with insomnia: Study protocol for a randomized, sham-controlled, subject-and-assessor-blinded trial.Frontiers in psychiatry · 2022Article
- Heart Rate Variability in Insulo-Opercular Epilepsy.Brain sciences · 2021Article
- Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals.Sensors (Basel, Switzerland) · 2021Article
- Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks.Scientific reports · 2021Article
- Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme.Sensors (Basel, Switzerland) · 2021Article
- Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses.Frontiers in physiology · 2020Article
- Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?Journal of clinical medicine · 2018Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one's PPG-based heart rate information.
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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.