ArticlePloS one2015
Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04335097 (Sensor Based Vital Signs Monitoring of Patients With Clinical Manifestation of Covid 19 Disease During Home Isolation, a Randomized Feasibility Study), which is not on this map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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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.
Sensor Based Vital Signs Monitoring of Patients With Clinical Manifestation of Covid 19 Disease During Home Isolation, a Randomized Feasibility Study
Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Detection of Hypertension Using Physiological Signals: A Review.International journal of environmental research and public health · 2021Pooled it
- Complexity-Based Measures of Heart Rate Dynamics in Older Adults Following Long- and Short-Term Tai Chi Training: Cross-sectional and Randomized Trial Studies.Scientific reports · 2019Trial
- The GRACE Cycle: A General Large-Language-Model Framework for Phenotype Discovery with Unknown Cluster Number.Research square · 2026Article
- Continuous blood pressure variability within young, healthy adults: a test-retest study to assess relative and absolute reliability.American journal of physiology. Heart and circulatory physiology · 2026Article
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- A Feasibility Study of Literature-Guided HRV Stratification Using Large Language Models.Diagnostics (Basel, Switzerland) · 2026Article
- Environmental and ethnic differences in short-term risk of first acute myocardial infarction: a prospective multiethnic cohort protocol.Frontiers in public health · 2026Article
- Non-invasive enhanced hypertension detection through ballistocardiograph signals with Mamba model.PeerJ. Computer science · 2025Article
- Article
- Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement.Sensors (Basel, Switzerland) · 2023Article
- A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective.Biomedical engineering online · 2023Review
- Effects of 8 months of high-intensity interval training on physical fitness and health-related quality of life in substance use disorder.Frontiers in psychiatry · 2023Article
- Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges.Bioengineering (Basel, Switzerland) · 2022Review
- Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review.Sensors (Basel, Switzerland) · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications.Neurocritical care · 2022Review
- Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system.Scientific reports · 2022Article
- Association between heart rate and cardiovascular death in patients with coronary heart disease: A NHANES-based cohort study.Clinical cardiology · 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
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients.
methodsA database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events.
resultsThe best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors.
conclusionsCombination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.
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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 Socratic graph.