Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection
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). We performed a retrospective analysis on 4 public Holter databases, analyzing the data of 72 normal subjects and 44 patient...
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Springer
2011
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| Online Access: | https://eprints.nottingham.ac.uk/1579/ |
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| author | Melillo, Paolo Fusco, Roberta Sansone, Mario Bracale, Marcello Pecchia, Leandro |
| author_facet | Melillo, Paolo Fusco, Roberta Sansone, Mario Bracale, Marcello Pecchia, Leandro |
| author_sort | Melillo, Paolo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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).
We performed a retrospective analysis on 4 public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, we adopted an exhaustive search of all possible combinations of HRV measures and we developed classifiers based on Classification and Regression Tree (CART) method, which is a non-parametric statistical technique.
We 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-minute segments of a 24-hour recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00% and 89.74% respectively.
Our results are comparable with other similar studies, but the method we 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. |
| first_indexed | 2025-11-14T18:15:43Z |
| format | Article |
| id | nottingham-1579 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:15:43Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-15792020-05-04T20:23:39Z https://eprints.nottingham.ac.uk/1579/ Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection Melillo, Paolo Fusco, Roberta Sansone, Mario Bracale, Marcello Pecchia, Leandro 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). We performed a retrospective analysis on 4 public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, we adopted an exhaustive search of all possible combinations of HRV measures and we developed classifiers based on Classification and Regression Tree (CART) method, which is a non-parametric statistical technique. We 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-minute segments of a 24-hour recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00% and 89.74% respectively. Our results are comparable with other similar studies, but the method we 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. Springer 2011-01 Article PeerReviewed Melillo, Paolo, Fusco, Roberta, Sansone, Mario, Bracale, Marcello and Pecchia, Leandro (2011) Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection. Medical and Biological Engineering and Computing, 49 (1). pp. 67-74. ISSN 0140-0118 Heart Rate Variability (HRV) Chronic Heart Failure (CHF) worsening assessment Classification and regression tree (CART) http://www.springerlink.com/content/c5739h26192h7762/ doi:10.1007/s11517-010-0728-5 doi:10.1007/s11517-010-0728-5 |
| spellingShingle | Heart Rate Variability (HRV) Chronic Heart Failure (CHF) worsening assessment Classification and regression tree (CART) Melillo, Paolo Fusco, Roberta Sansone, Mario Bracale, Marcello Pecchia, Leandro Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title | Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title_full | Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title_fullStr | Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title_full_unstemmed | Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title_short | Discrimination power of long-term heart rate variability measures for Chronic Heart Failure detection |
| title_sort | discrimination power of long-term heart rate variability measures for chronic heart failure detection |
| topic | Heart Rate Variability (HRV) Chronic Heart Failure (CHF) worsening assessment Classification and regression tree (CART) |
| url | https://eprints.nottingham.ac.uk/1579/ https://eprints.nottingham.ac.uk/1579/ https://eprints.nottingham.ac.uk/1579/ |