scholarly journals Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

2018 ◽  
Vol 154 ◽  
pp. 01038
Author(s):  
Achmad Rizal ◽  
Risanuri Hidayat ◽  
Hanung Adi Nugroho
Keyword(s):  
Respiratory Tract ◽  
Chaotic Behavior ◽  
The Self ◽  
Normal Lung ◽  
Complex Signal ◽  
Lung Sounds ◽  
Lung Sound ◽  
Self Similarity ◽  
Sufficient Strength ◽  
Degree Of Similarity

Lung sound is a complex signal produced by the respiratory process. The complex signal has several properties including a chaotic behavior, fractality or self-similarity property. One of lung sounds that arise from abnormalities occurred in the respiratory tract is pulmonary crackle sound. In this study, we tested the degree of self-similarity of pulmonary crackle sound and examined whether the degree of similarity can be used as a feature to differentiate the pulmonary lung crackle sound with normal lung sound. The results showed the sufficient strength of the self-similarity nature of the pulmonary crackle sound. Meanwhile, a test using K-mean clustering produced an accuracy of 87.5% to differentiate between the pulmonary crackle sound and normal lung sound. It can be stated then that it is deemed important to take another feature to obtain higher accuracy. The high self-similarity degree indicates that a pulmonary crackle sound has fractals properties.

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

2021 ◽  
Author(s):  
Sibghatullah I. Khan ◽  
Vikram Palodiya ◽  
Lavanya Poluboyina
Keyword(s):  
Phase Space ◽  
Human Lung ◽  
Feature Space ◽  
Normal Lung ◽  
Space Representation ◽  
Support Vector ◽  
Lung Sounds ◽  
Lung Sound ◽  
Phase Space Representation

Abstract Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

scholarly journals Analysis of Normal and Adventitious Lung Sound Signals Using Empirical Mode Decomposition and Central Tendency Measure

2021 ◽  
Vol 38 (3) ◽  
pp. 731-738
Author(s):  
Sibghatullah I. Khan ◽  
Ganjikunta Ganesh Kumar ◽  
Pandya Vyomal Naishadkumar ◽  
Sarvade Pedda Subba Rao
Keyword(s):  
Empirical Mode Decomposition ◽  
Sound Signal ◽  
Normal Lung ◽  
Chronic Obstructive ◽  
Central Tendency ◽  
Lung Sounds ◽  
Intrinsic Mode Functions ◽  
Lung Sound ◽  
Central Tendency Measure ◽  
Mode Decomposition

Diagnosing chronic obstructive pulmonary disease (COPD) from lung sounds is time consuming, onerous, and subjective to the expertise of pulmonologists. The preliminary diagnosis of COPD is often based on adventitious lung sounds (ALS). This paper proposes to objectively analyze the lung sound signals associated with COPD. Specifically, empirical mode decomposition (EMD), a data adaptive signal decomposition technique suitable for analyzing non-stationary signals, was adopted to decompose non-stationary lung sound signals. The use of EMD on lung sound signal results in intrinsic mode functions (IMFs), which are symmetric and band limited. The analytic IMFs were then computed through the Hilbert transform, which reveals the instantaneous frequency content of each IMF. The Hilbert transformed signal is analytic, and has a complex representation containing real and imaginary parts. Next, the central tendency measure (CTM) was introduced to quantify the circular shape of the analytical IMF plot. The result was taken as a useful feature to distinguish normal lung sound signal with ALS. Simulation results show that the CTM of analytic IMFs has a strong ability to distinguish between normal lung sound signals and ALS.

scholarly journals Multiscale tsallis entropy for pulmonary crackle detection

2018 ◽  
Vol 4 (3) ◽  
pp. 192 ◽  
Author(s):  
Achmad Rizal ◽  
Risanuri Hidayat ◽  
Hanung Adi Nugroho
Keyword(s):  
Signal Processing ◽  
Respiratory Tract ◽  
Digital Signal ◽  
Tsallis Entropy ◽  
Coarse Grained ◽  
Normal Lung ◽  
Lung Sound ◽  
Signal Complexity ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abnormalities in the lungs can be detected from the sound produced by the lungs. Diseases that occur in the lungs or respiratory tract can produce a distinctive lung sound. One of the examples of the lung sound is the pulmonary crackle caused by pneumonia or chronic bronchitis. Various digital signal processing techniques are developed to detect pulmonary crackle sound automatically, such as the measurement of signal complexity using Tsallis entropy (TE). In this study, TE measurements were performed through several orders on the multiscale pulmonary crackle signal. The pulmonary crackle signal was decomposed using the coarse-grained procedure since the lung sound as the biological signal had a multiscale property. In this paper, we used 21 pulmonary crackle sound and 22 normal lung sound for the experiment. The results showed that the second order TE on the scale of 1-15 had the highest accuracy of 97.67%. This result was better compared to the use of multi-order TE from the previous study, which resulted in an accuracy of 95.35%.

scholarly journals CAN AGN FEEDBACK BREAK THE SELF-SIMILARITY OF GALAXIES, GROUPS, AND CLUSTERS?

2014 ◽  
Vol 783 (1) ◽  
pp. L10 ◽  
Author(s):  
M. Gaspari ◽  
F. Brighenti ◽  
P. Temi ◽  
S. Ettori
Keyword(s):  
The Self ◽  
Self Similarity

scholarly journals On the self-similarity of diffracting gaseous detonations and the critical channel width problem

2021 ◽  
Vol 33 (6) ◽  
pp. 066106
Author(s):  
M. I. Radulescu ◽  
R. Mével ◽  
Q. Xiao ◽  
S. Gallier
Keyword(s):  
The Self ◽  
Channel Width ◽  
Self Similarity ◽  
Gaseous Detonations

Judged Beauty of Fractal Symmetries

2021 ◽  
pp. 027623742199469
Author(s):  
Jay Friedenberg ◽  
Preston Martin ◽  
Naomi Uy ◽  
Mackenzie Kvapil
Keyword(s):  
Local Symmetry ◽  
Self Similarity ◽  
Vertical Orientation ◽  
Degree Of Similarity ◽  
Different Levels

Fractals are patterns that show self-similarity at different levels of scale. Typically they appear in nature and this degree of similarity is approximate or statistical. However, artificial or exact fractals have also been studied and the advantage of these stimuli is the ability to more carefully control the relationships that occur across various hierarchies. In two experiments we studied the perceived beauty of a novel class of exact visual fractal in which we introduced reflection, rotation, translation, and random symmetries that repeated at a local and global levels. Rotation and reflection were consistently preferred to translation and randomness. Only reflected patterns were preferred at a vertical orientation. For all other symmetries there was no difference in preference between vertical and horizontal. In a second experiment we progressively eliminated the salience of local symmetry through opaque shading . Perceived beauty decreased with an increase in shading . For these patterns greater discriminability of their fractal quality makes them more aesthetically appealing.

scholarly journals A Community-Structure-Based Method for Estimating the Fractal Dimension, and its Application to Water Networks for the Assessment of Vulnerability to Disasters

2021 ◽  
Vol 35 (4) ◽  
pp. 1197-1210
Author(s):  
C. Giudicianni ◽  
A. Di Nardo ◽  
R. Greco ◽  
A. Scala
Keyword(s):  
Fractal Dimension ◽  
Community Structure ◽  
Distribution Systems ◽  
Water Distribution ◽  
Scaling Law ◽  
Vulnerability Index ◽  
The Self ◽  
Node Degree ◽  
Specific Response ◽  
Self Similarity

AbstractMost real-world networks, from the World-Wide-Web to biological systems, are known to have common structural properties. A remarkable point is fractality, which suggests the self-similarity across scales of the network structure of these complex systems. Managing the computational complexity for detecting the self-similarity of big-sized systems represents a crucial problem. In this paper, a novel algorithm for revealing the fractality, that exploits the community structure principle, is proposed and then applied to several water distribution systems (WDSs) of different size, unveiling a self-similar feature of their layouts. A scaling-law relationship, linking the number of clusters necessary for covering the network and their average size is defined, the exponent of which represents the fractal dimension. The self-similarity is then investigated as a proxy of recurrent and specific response to multiple random pipe failures – like during natural disasters – pointing out a specific global vulnerability for each WDS. A novel vulnerability index, called Cut-Vulnerability is introduced as the ratio between the fractal dimension and the average node degree, and its relationships with the number of randomly removed pipes necessary to disconnect the network and with some topological metrics are investigated. The analysis shows the effectiveness of the novel index in describing the global vulnerability of WDSs.

scholarly journals Onset of the Mach Reflection of Zel’dovich–von Neumann–Döring Detonations

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 314
Author(s):  
Tianyu Jing ◽  
Huilan Ren ◽  
Jian Li
Keyword(s):  
Hot Spot ◽  
Mach Reflection ◽  
Near Field ◽  
The Self ◽  
Small Scale ◽  
Mach Stem ◽  
Self Similarity ◽  
Von Neumann ◽  
Similarity Problem ◽  
Self Similar

The present study investigates the similarity problem associated with the onset of the Mach reflection of Zel’dovich–von Neumann–Döring (ZND) detonations in the near field. The results reveal that the self-similarity in the frozen-limit regime is strictly valid only within a small scale, i.e., of the order of the induction length. The Mach reflection becomes non-self-similar during the transition of the Mach stem from “frozen” to “reactive” by coupling with the reaction zone. The triple-point trajectory first rises from the self-similar result due to compressive waves generated by the “hot spot”, and then decays after establishment of the reactive Mach stem. It is also found, by removing the restriction, that the frozen limit can be extended to a much larger distance than expected. The obtained results elucidate the physical origin of the onset of Mach reflection with chemical reactions, which has previously been observed in both experiments and numerical simulations.

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