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Discrimination power of long-term heart rate variability measures for chronic heart failure detection
Paolo Melillo1, Roberta Fusco, Mario Sansone
1Department of Biomedical, Telecommunication and Electronic Engineering (DIBET), University of Naples Federico II, Naples, Italy.
Insights
Standard long-term heart rate variability (HRV) measures effectively discriminate chronic heart failure (CHF). The Classification and Regression Tree (CART) method identified key HRV metrics for accurate CHF diagnosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Informatics
Background:
- Chronic heart failure (CHF) diagnosis relies on various clinical and diagnostic tools.
- Heart Rate Variability (HRV) analysis offers a non-invasive method to assess autonomic nervous system function.
- Standard HRV measures require evaluation for their diagnostic utility in CHF.
Purpose of the Study:
- To investigate the discriminatory power of standard long-term HRV measures for diagnosing CHF.
- To identify optimal combinations of HRV metrics for CHF detection.
Main Methods:
- Retrospective analysis of Holter recordings from 72 healthy subjects and 44 CHF patients.
- Exhaustive search of HRV measure combinations.
- Development of classifiers using the Classification and Regression Tree (CART) method.
Main Results:
- The best combination of HRV measures included Total spectral power (TOTPWR), RMSSD, and SDANN.
- CART classifiers achieved 100.00% specificity and 89.74% sensitivity for CHF diagnosis.
- The CART method provided easily interpretable 'if...then...' classification rules.
Conclusions:
- Specific long-term HRV measures, identified via CART analysis, demonstrate high diagnostic accuracy for CHF.
- The CART approach offers a valuable, interpretable method for HRV-based disease classification.
- The derived classification rules align with existing clinical understanding of CHF.
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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