Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification

Wavelet Entropy (WE) is one of the entropy measurement methods by means of the discrete wavelet transform (DWT) subband. Some of the developments of WE are wavelet packet entropy (WPE), wavelet time entropy. WPE has several variations such as the Shannon entropy calculation on each subband of WPD that produces 2N entropy or WPE, which yields an entropy value. One of the WPE improvements is multilevel wavelet packet entropy (MWPE), which yields entropy value as much as N decomposition level. In a previous research, MWPE was calculated using Shannon method; hence, in this research MWPE calculation was done using Renyi and Tsallis method. The results showed that MWPE using Shannon calculation could yield the highest accuracy of 97.98% for N = 4 decomposition level. On the other hand, MWPE using Renyi entropy yielded the highest accuracy of 93.94% and the one using Tsallis entropy yielded 57.58% accuracy. Here, the test was performed on five lung sound data classes using multilayer perceptron as the classifier.

@article{Rizal2019,
  title     = {Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {2},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Achmad Rizal and Risanuri Hidayat and Hanung Adi Nugroho},
  doi       = {10.14569/IJACSA.2019.0100211},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100211}
}