Effect of different window and wavelet types on the performance of a novel crackle detection algorithm

Gorkem Serbes*, C. Okan Sakar, Yasemin P. Kahya, Nizamettin Aydin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection, which uses time- frequency and time-scale analysis, and the performance comparison for different window types in time-frequency analysis and also for different wavelet types in time-scale analysis is presented. In the proposed method, various feature sets are extracted using time-frequency and time-scale analysis for different windows and wavelet types. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data for different windows and wavelets are proposed.

Original languageEnglish
Title of host publicationConvergence and Hybrid Information Technology - 5th International Conference, ICHIT 2011, Proceedings
Pages575-581
Number of pages7
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011 - Daejeon, Korea, Republic of
Duration: 22 Sept 201124 Sept 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6935 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011
Country/TerritoryKorea, Republic of
CityDaejeon
Period22/09/1124/09/11

Keywords

  • complex wavelet
  • crackle detection
  • SVM
  • TF analysis

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