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Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

Abstract：

:Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Eslamizadeh G,Barati R

doi

10.1016/j.artmed.2017.05.005

subject

Has Abstract

pub_date

2017-05-01 00:00:00

pages

23-40

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(16)30542-5

journal_volume

78

pub_type

杂志文章

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