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All Journal Bulletin of Electrical Engineering and Informatics
A. Sidek, K.
International Islamic University Malaysia
Electrical & Electronics Engineering

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Distinctive Features for Normal and Crackles Respiratory Sounds using Cepstral Coefficients H. Mohd Johari, N.; Malik, Abdul; A. Sidek, K.
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Co-Authors A.A. Ketut Agung Cahyawan W H. Mohd Johari, N.