Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands

  • Rizal, Achmad (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Hidayat, Risanuri (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Nugroho, Hanung Adi (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada)
  • Received : 2017.12.13
  • Accepted : 2019.07.08
  • Published : 2019.10.31


Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.


Activity;Complexity;Hjorth Descriptor;Lung Sound;Mobility;Wavelet Transform


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