Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique

• Published in: Computers in biology and medicine Vol. 104; pp. 175 - 182
• Main Authors: Ulukaya, Sezer, Serbes, Gorkem, Kahya, Yasemin P.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2019; Elsevier Limited
• Subjects: Accuracy; Acoustics; Anomalies; Asthma; Chronic obstructive pulmonary disease; Classification; Classifiers; Conflicts of interest; Discrete Wavelet Transform; Discrimination; Humans; Learning algorithms; Lung - physiopathology; Lung diseases; Lung Diseases - physiopathology; Monophonic; Neural networks; Obstructive lung disease; Pathology; Polyphonic; Pulmonary sounds; Representations; Respiratory sounds; Respiratory Sounds - classification; Respiratory Sounds - physiopathology; Signal classification; Signal Processing, Computer-Assisted; State of the art; Support Vector Machine; Support vector machines; Wavelet Analysis; Wavelet transforms; Wheezing; Turkey; Monophonic; Polyphonic; Wheezing; Discrimination; Respiratory sounds; Pulmonary sounds

Wheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed m...