ArticleMedical & biological engineering & computing2011
Discrimination power of long-term heart rate variability measures for chronic heart failure detection.
Article in Medical & biological engineering & computing, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects.Bioengineering (Basel, Switzerland) · 2024Article
- CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2022Article
- An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review.Biomedicines · 2022Review
- Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure.Entropy (Basel, Switzerland) · 2021Article
- Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects.Journal of clinical medicine · 2021Article
- Novel Insights Into the Interaction Between the Autonomic Nervous System and Inflammation on Coronary Physiology: A Quantitative Flow Ratio Study.Frontiers in cardiovascular medicine · 2021Article
- Improving Accuracy of Heart Failure Detection Using Data Refinement.Entropy (Basel, Switzerland) · 2020Article
- Representation learning in intraoperative vital signs for heart failure risk prediction.BMC medical informatics and decision making · 2019Article
- Advances in Photopletysmography Signal Analysis for Biomedical Applications.Sensors (Basel, Switzerland) · 2018Review
- A new approach for analysis of heart rate variability and QT variability in long-term ECG recording.Biomedical engineering online · 2018Article
- Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study.Journal of medical systems · 2018Article
- Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.Computational and structural biotechnology journal · 2017Review
- Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients.Journal of medical systems · 2015Observational
- Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.Healthcare technology letters · 2015Article
- A multi-layer monitoring system for clinical management of Congestive Heart Failure.BMC medical informatics and decision making · 2015Article
- Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis.PloS one · 2015Article
- Prognostic significance of heart rate turbulence parameters in patients with chronic heart failure.BMC cardiovascular disorders · 2014Article
- User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner.BMC medical informatics and decision making · 2013Article
- Heart rate variability and target organ damage in hypertensive patients.BMC cardiovascular disorders · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to investigate the discrimination power of standard long-term heart rate variability (HRV) measures for the diagnosis of chronic heart failure (CHF). The authors performed a retrospective analysis on four public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, an exhaustive search of all possible combinations of HRV measures was adopted and classifiers based on Classification and Regression Tree (CART) method was developed, which is a non-parametric statistical technique. It was found that the best combination of features is: Total spectral power of all NN intervals up to 0.4 Hz (TOTPWR), square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) and standard deviation of the averages of NN intervals in all 5-min segments of a 24-h recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00 and 89.74%, respectively. The results are comparable with other similar studies, but the method used is particularly valuable because it provides an easy to understand description of classification procedures, in terms of intelligible "if … then …" rules. Finally, the rules obtained by CART are consistent with previous clinical studies.
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Registered trials
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