Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach

2019 ◽  
Vol 53 ◽  
pp. 101583 ◽  
Author(s):  
Alan Jovic ◽  
Karla Brkic ◽  
Goran Krstacic
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Feature Selection ◽  
Short Term ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals Effect of short term individualised training on heart rate variability in congestive heart failure

1998 ◽  
Vol 31 ◽  
pp. 199
Author(s):  
D. Curnier ◽  
M. Galinier ◽  
M. Bousquet ◽  
S. Boveda ◽  
B. Bazile ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Short Term

scholarly journals The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fausto Lucena ◽  
Allan Kardec Barros ◽  
Noboru Ohnishi
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Healthy Subjects ◽  
Matching Pursuit ◽  
Classification Performance ◽  
Short Term ◽  
Gabor Functions ◽  
Nearest Neighbour Classifier

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm andk-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.

scholarly journals A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures

PLoS ONE ◽  
2014 ◽  
Vol 9 (4) ◽  
pp. e93399 ◽  
Author(s):  
Guanzheng Liu ◽  
Lei Wang ◽  
Qian Wang ◽  
GuangMin Zhou ◽  
Ying Wang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Short Term ◽  
New Approach

scholarly journals A10-3 Heart rate variability and heart rate early changes induced by ventricular pacing in advances congestive heart failure

EP Europace ◽  
2003 ◽  
Vol 4 (Supplement_2) ◽  
pp. B15-B15
Author(s):  
F. Giraldi ◽  
M. Kloss ◽  
C. Fantoni ◽  
F. Regolip ◽  
H. Klein ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Ventricular Pacing
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