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Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis
Paolo Melillo1, Raffaele Izzo2, Ada Orrico1
1Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy; SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
Insights
Heart Rate Variability (HRV) analysis using data-mining models can effectively identify hypertensive patients at high risk for vascular events. This approach offers a more reliable tool for automatic risk stratification compared to traditional methods.
Area of Science:
- Cardiology
- Medical Informatics
- Data Science
Background:
- Heart Rate Variability (HRV) is recognized as a significant indicator of vascular event risk.
- The precise predictive capability of HRV for vascular events requires further elucidation.
- Hypertensive patients need improved tools for automatic risk stratification.
Purpose of the Study:
- To develop novel predictive models for vascular events in hypertensive patients.
- To create an automatic risk stratification tool utilizing data-mining algorithms.
- To assess the efficacy of HRV in predicting vascular events.
Main Methods:
- Collected data from 139 hypertensive patients with at least 12 months of follow-up.
- Employed data-mining algorithms including support vector machine, tree-based classifiers, and artificial neural networks.
- Evaluated classifier accuracy using receiver-operator characteristic curves and compared with echographic parameters.
Main Results:
- The random forest model demonstrated the highest predictive performance.
- The best model achieved 71.4% sensitivity and 87.8% specificity in identifying high-risk patients.
- HRV-based models outperformed conventional echographic parameters in predicting vascular events.
Conclusions:
- Combining Heart Rate Variability measures with data-mining algorithms provides a reliable method for risk stratification.
- This approach can effectively identify hypertensive patients at high risk of future vascular events.
- Automated HRV analysis offers a promising tool for proactive cardiovascular risk management.
Background:
There 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.
Methods:
A 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.
Results:
The 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.
Conclusions:
Combination 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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