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Integrated Approach for Automatic Crackle Detection Based on Fractal Dimension and Box Filtering

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  • Cátia Pinho

    (Instituto de Telecomunicações (IT), University of Aveiro, Aveiro, Portugal)

  • Ana Oliveira

    (School of Health Sciences (ESSUA), University of Aveiro, Aveiro, Portugal)

  • Cristina Jácome

    (School of Health Sciences (ESSUA), University of Aveiro, Aveiro, Portugal)

  • João Manuel Rodrigues

    (Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal)

  • Alda Marques

    (School of Health Sciences (ESSUA), University of Aveiro, Aveiro, Portugal)

Abstract

Crackles are adventitious respiratory sounds (RS) that provide valuable information on different respiratory conditions. Nevertheless, crackles automatic detection in RS is challenging, mainly when collected in clinical settings. This study aimed to develop an algorithm for automatic crackle detection/characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on 4 main procedures: i) recognition of a potential crackle; ii) verification of its validity; iii) characterisation of crackles parameters; and iv) optimisation of the algorithm parameters. Twenty-four RS 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 a multi-annotator gold standard agreement. High level of overall performance (F-score=92%) was achieved. The results highlight the potential of the algorithm for automatic crackle detection and characterisation of RS acquired in clinical settings.

Suggested Citation

  • Cátia Pinho & Ana Oliveira & Cristina Jácome & João Manuel Rodrigues & Alda Marques, 2016. "Integrated Approach for Automatic Crackle Detection Based on Fractal Dimension and Box Filtering," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 5(4), pages 34-50, October.
    • Handle: RePEc:igg:jrqeh0:v:5:y:2016:i:4:p:34-50
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