Feature extraction and classification of heart sound based on autoregressive power spectral density (AR-PSD)

Author

Saputra, Laurentius Kuncoro Probo ; Nugroho, Hanung Adi ; Wulandari, Meirista

Author_Institution

Dept. of Electr. Eng. & Inf. Technol., Gajah Mada Univ., Yogyakarta, Indonesia

fYear

2014

Firstpage

139

Lastpage

143

Abstract

Heart sound has an important information that can help in diagnosis of the abnormality. This paper is developed based on the previous research to improve the feature in each types of abnormal heart sound. Wavelet decomposition is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. Finally 13 types of abnormal heart sound are classified into 13 categories. Data set of heart sound is taken from Michigan Sound Heart Database. In this research, magnitude of frequency and kurtosis are used as additional features. The result shows that classifier system achives the accuracy of 92.31%.

Keywords

cardiology; feature extraction; medical signal processing; patient diagnosis; pattern classification; signal classification; wavelet transforms; Michigan sound heart database; abnormal heart sound; autoregressive power spectral density; classifier system; frequency magnitude; heart sound classification; heart sound feature extraction; kurtosis magnitude; noise removal; wavelet decomposition; Accuracy; Expert systems; Feature extraction; Heart; Multilayer perceptrons; Noise; Wavelet transforms; AR-PSD; heart sound abnormality; wavelet decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, Computer and Electrical Engineering (ICITACEE), 2014 1st International Conference on

Print_ISBN

978-1-4799-6431-4

Type

conf

DOI

10.1109/ICITACEE.2014.7065730

Filename

7065730