Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds
Joint Authors
Mondal, Ashok
Bhattacharya, Parthasarathi
Saha, Goutam
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation.
Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice.
In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals.
The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis).
These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network.
The results are obtained using 5-fold cross-validation.
The performance of the proposed method is compared with a wavelet analysis based method.
The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.
American Psychological Association (APA)
Mondal, Ashok& Bhattacharya, Parthasarathi& Saha, Goutam. 2014. Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1048630
Modern Language Association (MLA)
Mondal, Ashok…[et al.]. Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1048630
American Medical Association (AMA)
Mondal, Ashok& Bhattacharya, Parthasarathi& Saha, Goutam. Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1048630
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048630