Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/22430
Title: Automatic crackle detection algorithm based on fractal dimension
Author: Pinho, Cátia
Oliveira, Ana
Jácome, Cristina
Rodrigues, João
Marques, Alda
Keywords: Adventitious respiratory sounds
Crackles
Automatic detection/classification algorithms
Fractal dimension
Box filtering
Issue Date: 2015
Publisher: Elsevier
Abstract: Crackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings.
Peer review: yes
URI: http://hdl.handle.net/10773/22430
DOI: 10.1016/j.procs.2015.08.592
ISSN: 1877-0509
Appears in Collections:DETI - Artigos
IEETA - Artigos
IT - Artigos
Lab3R - Artigos

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