Abstract
Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.
Keywords
Auscultation
Wavelet
Pattern recognition (psychology)
Speech recognition
Wavelet transform
Respiratory sounds
Computer science
Sound (geography)
Support vector machine
Artificial intelligence
Artificial neural network
Acoustics
Medicine
Radiology
Physics
Subject Areas
Phonocardiography and Auscultation Techniques
·Pulmonary and Respiratory Medicine ·Health Sciences
Citations by Year
2015: 1
2016: 1
2018: 2
2020: 1
2021: 2
2022: 2
2024: 1
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