Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

43rd International Conference on Telecommunications and Signal Processing

ISBN

978-1-7281-6377-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

545-549

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Milan, Italy

Datum konání akce

7. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000577106400117