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Artificial Intelligence Applications on Classification of Heart Sounds

Artificial Intelligence Applications on Classification of Heart Sounds

Huseyin Coskun, Tuncay Yigit
Copyright: © 2018 |Pages: 38
ISBN13: 9781522547693|ISBN10: 152254769X|EISBN13: 9781522547709
DOI: 10.4018/978-1-5225-4769-3.ch007
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MLA

Coskun, Huseyin, and Tuncay Yigit. "Artificial Intelligence Applications on Classification of Heart Sounds." Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems, edited by Utku Kose, et al., IGI Global, 2018, pp. 146-183. https://doi.org/10.4018/978-1-5225-4769-3.ch007

APA

Coskun, H. & Yigit, T. (2018). Artificial Intelligence Applications on Classification of Heart Sounds. In U. Kose, G. Guraksin, & O. Deperlioglu (Eds.), Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems (pp. 146-183). IGI Global. https://doi.org/10.4018/978-1-5225-4769-3.ch007

Chicago

Coskun, Huseyin, and Tuncay Yigit. "Artificial Intelligence Applications on Classification of Heart Sounds." In Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems, edited by Utku Kose, Gur Emre Guraksin, and Omer Deperlioglu, 146-183. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-4769-3.ch007

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Abstract

The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artificial neural network (ANN) methods have been used for classification of these heart sounds. Using wavelet and MFCC features, classification success of SVM has been obtained as 93.33% and 100%, respectively. Using wavelet and MFCC features, classification success of ANN has been obtained as 83.33% and 90%, respectively.

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