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Pulmonary crackle detection using the Hilbert energy envelope

Pulmonary crackle detection using the Hilbert energy envelope
Pulmonary crackle detection using the Hilbert energy envelope
This paper presents a method for automatic pulmonary crackle detection based on the Hilbert energy envelope (HEE). Automatic detection of crackles in lung sounds offers a non-invasive way of monitoring or diagnosing cardiopulmonary diseases. The algorithm is divided into four main steps: (a) pre-processing, (b) estimation of HEE, (c) thresholding, and (d) applying time width conditions based on crackle two-cycle deflection and initial deflection width. Its performance is tested using a publicly available lung sound dataset of fine and coarse crackles and evaluated by the sensitivity (95.7%), positive predictive value (89.5%), and F-score (91.7%) for crackle detection. The good detection performance indicates the potential of the HEE-based algorithm as an automatic method for crackle detection in lung sound recordings.
Automatic pulmonary crackle detection, Hilbert Energy Envelope (HEE) Algorithm, Pulmonary crackle
1680-0737
994-1003
Springer, Cham
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
Jarm, Tomaz
Cvetkoska, Aleksandra
Mahnič-Kalamiza, Samo
Miklavcic, Damijan
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
Jarm, Tomaz
Cvetkoska, Aleksandra
Mahnič-Kalamiza, Samo
Miklavcic, Damijan

Pal, Ravi and Barney, Anna (2020) Pulmonary crackle detection using the Hilbert energy envelope. Jarm, Tomaz, Cvetkoska, Aleksandra, Mahnič-Kalamiza, Samo and Miklavcic, Damijan (eds.) In 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020. Springer, Cham. pp. 994-1003 . (doi:10.1007/978-3-030-64610-3_111).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper presents a method for automatic pulmonary crackle detection based on the Hilbert energy envelope (HEE). Automatic detection of crackles in lung sounds offers a non-invasive way of monitoring or diagnosing cardiopulmonary diseases. The algorithm is divided into four main steps: (a) pre-processing, (b) estimation of HEE, (c) thresholding, and (d) applying time width conditions based on crackle two-cycle deflection and initial deflection width. Its performance is tested using a publicly available lung sound dataset of fine and coarse crackles and evaluated by the sensitivity (95.7%), positive predictive value (89.5%), and F-score (91.7%) for crackle detection. The good detection performance indicates the potential of the HEE-based algorithm as an automatic method for crackle detection in lung sound recordings.

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More information

Published date: 30 November 2020
Keywords: Automatic pulmonary crackle detection, Hilbert Energy Envelope (HEE) Algorithm, Pulmonary crackle

Identifiers

Local EPrints ID: 445670
URI: http://eprints.soton.ac.uk/id/eprint/445670
ISSN: 1680-0737
PURE UUID: 6e3da2d1-6ea9-4128-8918-40620bad5fca
ORCID for Anna Barney: ORCID iD orcid.org/0000-0002-6034-1478

Catalogue record

Date deposited: 06 Jan 2021 17:30
Last modified: 18 Feb 2021 16:51

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Contributors

Author: Ravi Pal
Author: Anna Barney ORCID iD
Editor: Tomaz Jarm
Editor: Aleksandra Cvetkoska
Editor: Samo Mahnič-Kalamiza
Editor: Damijan Miklavcic

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