Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability

Melillo, Paolo; De Luca, Nicola; Bracale, Marcello; Pecchia, Leandro

Abstract

This study aims to develop an automatic classifier for risk assessment in patients suffering from congestive heart failure (CHF). The proposed classifier separates lower risk patients from higher risk ones, using standard long-term heart rate variability (HRV) measures. Patients are labeled as lower or higher risk according to the New York Heart Association classification (NYHA). A retrospective analysis on two public Holter databases was performed, analyzing the data of 12 patients suffering from mild CHF (NYHA I and II), labeled as lower risk, and 32 suffering from severe CHF (NYHA III and IV), labeled as higher risk. Only patients with a fraction of total heartbeats intervals (RR) classified as normal-to-normal (NN) intervals (NN/RR) higher than 80% were selected as eligible in order to have a satisfactory signal quality. Classification and regression tree (CART) was employed to develop the classifiers. A total of 30 higher risk and 11 lower risk patients were included in the analysis. The proposed classification trees achieved a sensitivity and a specificity rate of 93.3% and 63.6%, respectively, in identifying higher risk patients. Finally, the rules obtained by CART are comprehensible and consistent with the consensus showed by previous studies that depressed HRV is a useful tool for risk assessment in patients suffering from CHF.

• Journal Article Type: Article
• Publication Date: May 1, 2013
• Deposit Date: Apr 2, 2014
• Publicly Available Date: Apr 2, 2014
• Journal: IEEE Journal of Biomedical and Health Informatics
• Electronic ISSN: 2168-2194
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 3
• DOI: https://doi.org/10.1109/JBHI.2013.2244902
• Public URL: https://nottingham-repository.worktribe.com/output/1002159
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6449272