Authors:
Rima Touahria
1
;
Abdenour Hacine-Gharbi
1
;
Philippe Ravier
2
and
Messaoud Mostefai
1
Affiliations:
1
LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria
;
2
PRISME Laboratory, University of Orleans, 12 rue de Blois, 45067 Orleans, France
Keyword(s):
Heart Sound, Multidomain Features, Feature Extraction, Feature Selection, Mutual Information, Classification.
Abstract:
Many classification systems of the heart sound signals use a combination of features from different domains. In a former reference paper, 324 multidomain features were used for classifying segmented phonocardiogram signals. However, the large feature dimension requires high memory space, high calculus and probably reduces the classification accuracy caused by the curse of dimensionality. In the present work, we propose to reduce the dimensionality of features vectors by selecting the relevant features using six heuristic strategies of feature selection based on mutual information maximisation criterion. In order to validate the selected subset of features, a k-NN model based-classifier was used and evaluated on the PhysioNet/Computing in Cardiology Challenge2016 dataset using the same features sets described in the reference paper. The results demonstrate that the Joint Mutual Information (JMI) selection strategy increases the classification rate from 85. 57% to 89.28% and simultaneo
usly reduces dimension from 324 to 46. Furthermore, this work demonstrates that systolic segment features are the most relevant for murmur/normal classification. It also demonstrates the capability of feature selection algorithms to emphasize specific key areas in signals, which is helpful for aided diagnostic systems and fundamental research.
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