Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability
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...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2013
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| Online Access: | https://eprints.nottingham.ac.uk/2822/ |
| _version_ | 1848790884653465600 |
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| author | Melillo, Paolo De Luca, Nicola Bracale, Marcello Pecchia, Leandro |
| author_facet | Melillo, Paolo De Luca, Nicola Bracale, Marcello Pecchia, Leandro |
| author_sort | Melillo, Paolo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:19:43Z |
| format | Article |
| id | nottingham-2822 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:19:43Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-28222020-05-04T20:19:18Z https://eprints.nottingham.ac.uk/2822/ 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 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. Institute of Electrical and Electronics Engineers 2013-05 Article PeerReviewed Melillo, Paolo, De Luca, Nicola, Bracale, Marcello and Pecchia, Leandro (2013) Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE Journal of Biomedical and Health Informatics, 17 (3). pp. 727-733. ISSN 2168-2194 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6449272 doi:10.1109/JBHI.2013.2244902 doi:10.1109/JBHI.2013.2244902 |
| spellingShingle | Melillo, Paolo De Luca, Nicola Bracale, Marcello Pecchia, Leandro Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title | Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title_full | Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title_fullStr | Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title_full_unstemmed | Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title_short | Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| title_sort | classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability |
| url | https://eprints.nottingham.ac.uk/2822/ https://eprints.nottingham.ac.uk/2822/ https://eprints.nottingham.ac.uk/2822/ |