The Library
Discrimination power of short-term heart rate variability measures for CHF assessment
Tools
Pecchia, Leandro, Melillo, Paolo, Sansone, Mario and Bracale, Marcello (2011) Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Transactions on Information Technology in Biomedicine, Volume 15 (Number 1). pp. 40-46. doi:10.1109/TITB.2010.2091647 ISSN 1089-7771.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1109/TITB.2010.2091647
Abstract
In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3% and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7% and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible “if ... then ...” rules.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||
Journal or Publication Title: | IEEE Transactions on Information Technology in Biomedicine | ||||
Publisher: | IEEE | ||||
ISSN: | 1089-7771 | ||||
Official Date: | 2011 | ||||
Dates: |
|
||||
Volume: | Volume 15 | ||||
Number: | Number 1 | ||||
Page Range: | pp. 40-46 | ||||
DOI: | 10.1109/TITB.2010.2091647 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |