Multilevel wavelet packet entropy: A new strategy for lung sound feature extraction based on wavelet entropy

Rizal, Achmad; Hidayat, Risanuri; Nugroho, Hanung Adi

doi:10.1109/icoras.2017.8308048

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…Since WPE was calculated on multilevel, for N level decomposition it will produce N entropy values as the signal feature. The experiments reported 97.98% accuracy using Db8 at the level of decomposition N = 4 [8].…”

Section: Related Workmentioning

confidence: 99%

“…The number of generated features was 2 N , where N refers to the signal decomposition level. In another study, multilevel wavelet packet entropy (MWPE) was proposed for lung sound analysis [8]. If in [16], WPE was produced by calculating the Shannon entropy on each WPD subband, then in [8], WPE was generated from Shannon entropy calculations from the subband relative energy such as WE calculation in [5].…”

Section: Related Workmentioning

confidence: 99%

“…In this study, we used lung sound recording data obtained from several sources [18] [19]. The same data was used in previous studies [8] [20]. Each pulmonary sound data consisted of single breathing cycle, inspiration, and expiration.…”

Section: A Lung Sound Datamentioning

confidence: 99%

“…Previous research has proposed a multilevel wavelet packet entropy (MWPE) method for pulmonary sound feature extraction [8]. Entropy was calculated on the subband of WPD at several levels using the Shannon entropy method.…”

Section: Introductionmentioning

confidence: 99%

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

Rizal¹,

Hidayat²,

Nugroho³

2019

ijacsa

Self Cite

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Lung Sound Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Rizal¹,

Hidayat²,

Nugroho³

2019

ijacsa

Self Cite

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“…The wavelet is a smooth and quickly vanishing oscillating function with good localization in both frequency and time [18]. A wavelet family wf a,b is the set of elementary functions generated by dilations and translations of a unique admissible mother wavelet wf (t):…”

Section: Wavelet Entropymentioning

confidence: 99%

Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine

Bao¹,

Nakamura²

2018

Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract:In order to identify Ford vehicles from non-Ford vehicles, this paper proposed a novel method based on the combination of wavelet entropy, particle swarm optimization, and support vector machine. We collect a 100-image dataset, 50 are Ford vehicles and the rest 50 are non-Ford vehicles. The results show that our method obtained a sensitivity of 82.20± 3.94%, a specificity of 81.60± 3.50%, and an accuracy of 81.90± 0.74%. In all, this method is promising in vehicle identification.

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Complexity Based Multilevel Signal Analysis for Epileptic Seizure Detection

Wijayanto

Hartanto

Nugroho

2020

2020 3rd International Conference on Information and Communications Technology (ICOIACT)

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Multilevel wavelet packet entropy: A new strategy for lung sound feature extraction based on wavelet entropy

Cited by 11 publications

References 13 publications

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

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

Vehicle Identification Using Wavelet Entropy and Particle Swarm Optimization Support Vector Machine

Complexity Based Multilevel Signal Analysis for Epileptic Seizure Detection

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