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
994-1003
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
30 November 2020
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
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.
.
(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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Pulmonary Crackle Detection
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Pulmonary Crackle Detection using the Hilbert Energy Envelope
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Published date: 30 November 2020
Additional Information:
This research was supported by the NIHR Southampton Biomedical Research Centre, AAIR Charity and the Engineering and Physical Sciences Research Council.
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
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Date deposited: 06 Jan 2021 17:30
Last modified: 17 Mar 2024 06:08
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Contributors
Editor:
Tomaz Jarm
Editor:
Aleksandra Cvetkoska
Editor:
Samo Mahnič-Kalamiza
Editor:
Damijan Miklavcic
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