Abstract

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.

Highlights

  • Abnormalities that occur in the respiratory system can be observed from the sound generated during the respiratory process

  • We used the level of decomposition N = 7 so that the subband can be as wide as 31.75 Hz

  • This paper describes the variation of multilevel wavelet packet entropy (MWPE) calculations using Shannon entropy, Renyi entropy, and Tsallis entropy

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Summary

INTRODUCTION

Abnormalities that occur in the respiratory system can be observed from the sound generated during the respiratory process. Various entropy calculation methods have been used in lung sound analysis in which Sample entropy was used as a feature for detecting pulmonary sound status using morphological complexities [1]. Multiscale entropy was reported to be better in distinguishing lung sounds in alveolitis patients rather than spectral or statistical methods [4]. Another entropy measurement method is the wavelet entropy (WE). Previous research has proposed a multilevel wavelet packet entropy (MWPE) method for pulmonary sound feature extraction [8]. MWPE was calculated using Renyi entropy and Tsallis entropy to observe the resulted accuracy.

RELATED WORKS
MATERIAL AND METHODS
Lung Sound Data
Wavelet Packet Entropy
Classifier and Validation
RESULTS AND DISCUSSION
CONCLUSION
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