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Integrated Approach for Automatic Crackle Detection Based on Fractal Dimension and Box Filtering
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Author(s): Cátia Pinho (University of Aveiro, Portugal), Ana Oliveira (University of Aveiro, Portugal), Cristina Jácome (University of Porto, Portugal & University of Aveiro, Portugal), João Manuel Rodrigues (University of Aveiro, Portugal)and Alda Marques (University of Aveiro, Portugal)
Copyright: 2020
Pages: 18
Source title:
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch043
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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.
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