Research Article Open Access

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Achmad Rizal1, Risanuri Hidayat2 and Hanung Adi Nugroho2
  • 1 School of Electrical Engineering, Telkom University, Bandung, Indonesia
  • 2 Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia


Lung sound is produced by the respiration process in the human respiratory tract. It contains information about the health of the respiratory organs. Lung sound is non-stationary signals and complex signals. One method for the analysis of non-stationary signals often used for the analysis of lung sounds is Empirical Mode Decomposition (EMD). EMD is used to view the Instantaneous Frequency (IF) of the lung sound to differentiate the types of lung sounds. Features extraction directly on Intrinsic Mode Function (IMF) of EMD result is rarely performed in the lung sound analysis. In this research, the EMD was used to obtain IMF of lung sounds. IMF from lung sounds was then analyzed using the Hjorth descriptors. As a classifier, we used Multilayer Perceptron (MLP) with a three-fold cross validation (3fold CV) for validation. From the test, it was found that activity parameter in the first 10 IMF yielded 98.8% accuracy on five classes of data tested. The proposed method showed the excellence of the measurement of the Hjorth descriptor on IMF for feature extraction in lung sound classification.

American Journal of Applied Sciences
Volume 14 No. 1, 2017, 166-173


Submitted On: 1 April 2016 Published On: 27 January 2017

How to Cite: Rizal, A., Hidayat, R. & Nugroho, H. A. (2017). Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor. American Journal of Applied Sciences, 14(1), 166-173.

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  • Empirical Mode Decomposition
  • Intrinsic Mode Function
  • The Hjorth Descriptor
  • Lung Sound
  • Feature Extraction