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

Achmad Rizal, Risanuri Hidayat and Hanung Adi Nugroho
Volume: 15, No: 5, Page: 1068 ~ 1081, Year: 2019
10.3745/JIPS.02.0116
Keywords: Activity, Complexity, Hjorth Descriptor, Lung Sound, Mobility, Wavelet Transform
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Abstract
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 multiscale Hjorth descriptor as in previous studies.

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Cite this article
IEEE Style
A. Rizal, R. Hidayat and H. A. Nugroho, "Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1068~1081, 2019. DOI: 10.3745/JIPS.02.0116.

ACM Style
Achmad Rizal, Risanuri Hidayat, and Hanung Adi Nugroho. 2019. Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands, Journal of Information Processing Systems, 15, 5, (2019), 1068~1081. DOI: 10.3745/JIPS.02.0116.