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

Authors
  • Ulukaya, Sezer1
  • Serbes, Gorkem2
  • Kahya, Yasemin P3
  • 1 Department of Electrical and Electronics Engineering, Boǧaziçi University, 34342, Istanbul, Turkey; Department of Electrical and Electronics Engineering, Trakya University, 22030, Edirne, Turkey. Electronic address: [email protected] , (Turkey)
  • 2 Department of Biomedical Engineering, Yildiz Technical University, 34220, Istanbul, Turkey. Electronic address: [email protected] , (Turkey)
  • 3 Department of Electrical and Electronics Engineering, Boǧaziçi University, 34342, Istanbul, Turkey. Electronic address: [email protected] , (Turkey)
Type
Published Article
Journal
Computers in biology and medicine
Publication Date
Jan 01, 2019
Volume
104
Pages
175–182
Identifiers
DOI: 10.1016/j.compbiomed.2018.11.004
PMID: 30496939
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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 method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods. An optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers. The results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM. It is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

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