Title :
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
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;
Conference_Titel :
Information Technology, Computer and Electrical Engineering (ICITACEE), 2014 1st International Conference on
Print_ISBN :
978-1-4799-6431-4
DOI :
10.1109/ICITACEE.2014.7065730
