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

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

doi:10.14569/ijacsa.2019.0100211

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…In 1948, Shannon introduced the concept of information entropy as a means of quantifying information [50]. In the field of nonlinear time series, it serves as a measure of information uncertainty [51].…”

Section: Wavelet Packet Energy and Shannon Entropymentioning

confidence: 99%

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

Wang,

Zhang,

et al. 2024

Applied Sciences

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Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands.

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Section: Wavelet Packet Energy and Shannon Entropymentioning

confidence: 99%

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

Wang,

Zhang,

et al. 2024

Applied Sciences

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“…Five classes of RS were countered, i.e., normal, wheeze, crackle, stridor, and squawk. Discontinuous abnormal respiratory sound includes fine crackle, and coarse crackle was not considered ( 21 ).…”

Section: Related Workmentioning

confidence: 99%

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

et al. 2021

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Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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“…A. Rizal et al in [15] presented a new feature extraction of lung sound, which is multilevel wavelet packet entropy (MWPE) calculations using Shannon entropy, Renyi entropy, and Tsallis entropy. The test was performed on five classes of lung sound databases.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Entropy to enhance the structure of the Wavelet-Packets-Best-Tree for Automatic Speech Recognition

Mousa¹

2021

The Egyptian Journal of Language Engineering

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Best Tree Encoding (BTE) is a promising feature extraction technique based on wavelet packet decomposition that is utilized in Automatic Speech Recognition (ASR). This research introduces an enhancement of Wavelet Packet Best Tree (WPBT) Calculations. The standard features BTE encodes the tree structure using a mathematical model into a features vector of 4 components. The best tree structure has been calculated using the entropy function. In the standard version of BTE, Shannon entropy has been chosen as the entropy function. In this research, Shannon Entropy (SE), Renyi Entropy (RE), and Tsallis Entropy (TE) are used to construct the Best Tree. The encoding of the Best Tree has been done using the same mathematical model approach in the standard 4-Point BTE. The proposed model is tested and Verified against the most widely used feature Mel Frequency Cepstral Coefficient (MFCC) plus delta and delta-delta coefficients (39 parameters) to evaluate its performance. The TIMIT database is used in this research. All phones are divided into five classes: Vowels, Fricatives, Silences, Nasals, and Plosives. The acoustical model has been implemented using Hidden Markov Model (HMM). No language model has been applied. The HMM Tool Kit (HTK) software is used for model implementation. The experiments show that BTE using Tsallis entropy yields the highest overall success rate of 75.85% which is better than MFCC's overall success rate of 71.76%. Comparing the vector of 4 components of BTE to the 39 components vector of MFCC makes it a very promising feature vector to be considered for research and development.

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

Cited by 5 publications

References 16 publications

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Optimal Entropy to enhance the structure of the Wavelet-Packets-Best-Tree for Automatic Speech Recognition

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