Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137725" target="_blank" >RIV/00216305:26220/20:PU137725 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9163565" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9163565</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163565" target="_blank" >10.1109/TSP49548.2020.9163565</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform
Original language description
Respiratory signals emanating from human lungs give vital and indicative information regarding the health status of a patient’s lungs. Conventional clinical methods require professional pulmonologists to diagnose such signals properly and are also time consuming. In this proposed work, an efficient and automated method is proposed for the diagnosis and classification of respiratory signals into normal and abnormal respiratory sound. Respiratory signal is cleaned using a band pass filter, followed by features extraction in wavelet domain. Discriminatory features from the filtered signals are fed to SVM for purpose of classification of signals. Proposed methodology has achieved an accuracy of 92.30% in correctly classifying the pathological lung sounds. Outcomes of the proposed algorithm are promising and indicates its usability for some real time application.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
43rd International Conference on Telecommunications and Signal Processing
ISBN
978-1-7281-6377-2
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
545-549
Publisher name
Neuveden
Place of publication
Neuveden
Event location
Milan, Italy
Event date
Jul 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000577106400117