PMID- 21075731
OWN - NLM
STAT- MEDLINE
DCOM- 20110421
LR  - 20110110
IS  - 1558-0032 (Electronic)
IS  - 1089-7771 (Linking)
VI  - 15
IP  - 1
DP  - 2011 Jan
TI  - Discrimination power of short-term heart rate variability measures for CHF 
      assessment.
PG  - 40-6
LID - 10.1109/TITB.2010.2091647 [doi]
AB  - 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 DeltaAVNN and 
      DeltaLF/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.
FAU - Pecchia, Leandro
AU  - Pecchia L
AD  - Department of Biomedical, Electronic, and Telecommunication Engineering, 
      University of Naples Federico II, Naples 80128, Italy. leandro.pecchia@ unina.it
FAU - Melillo, Paolo
AU  - Melillo P
FAU - Sansone, Mario
AU  - Sansone M
FAU - Bracale, Marcello
AU  - Bracale M
LA  - eng
PT  - Journal Article
DEP - 20101111
PL  - United States
TA  - IEEE Trans Inf Technol Biomed
JT  - IEEE transactions on information technology in biomedicine : a publication of the 
      IEEE Engineering in Medicine and Biology Society
JID - 9712259
SB  - IM
MH  - Adult
MH  - Aged
MH  - Algorithms
MH  - Area Under Curve
MH  - Chronic Disease
MH  - Data Mining
MH  - Electrocardiography/*methods
MH  - Female
MH  - Heart Failure/*diagnosis/physiopathology
MH  - Heart Rate/*physiology
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - ROC Curve
MH  - Regression Analysis
MH  - Reproducibility of Results
MH  - *Signal Processing, Computer-Assisted
EDAT- 2010/11/16 06:00
MHDA- 2011/04/22 06:00
CRDT- 2010/11/16 06:00
PHST- 2010/11/16 06:00 [entrez]
PHST- 2010/11/16 06:00 [pubmed]
PHST- 2011/04/22 06:00 [medline]
AID - 10.1109/TITB.2010.2091647 [doi]
PST - ppublish
SO  - IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):40-6. doi: 
      10.1109/TITB.2010.2091647. Epub 2010 Nov 11.