Authors:
Ana Oliveira
1
;
Cátia Pinho
2
;
João Dinis
3
;
Daniela Oliveira
1
and
Alda Marques
1
Affiliations:
1
University of Aveiro (ESSUA), Portugal
;
2
University of Aveiro, Portugal
;
3
University of Aveiro (ESSUA) and University of Aveiro, Portugal
Keyword(s):
Time Frequency Analysis, Wheezing, Lung Function Testing, Lower Respiratory Tract Infection.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Clinical Problems and Applications
;
Health Information Systems
;
Software Systems in Medicine
;
Therapeutic Systems and Technologies
Abstract:
The automatic detection of wheeze offers the potential for diagnosing and monitoring respiratory diseases, e.g., lower respiratory tract infection (LRTI). By determining the relationship between wheeze detection and other lung function data, it is possible to develop a more sensitive tool for detecting respiratory conditions. This pilot study aimed to: i) explore the robustness of a time frequency wheeze detector (TF-WD) and ii) describe the correlation between wheezing and spirometry parameters. Lung sounds and spirometry parame-ters were acquired from six outpatients with LRTI (five with right lung infection). Number, fundamental frequency and duration of wheezes were obtained through a TF-WD algorithm. The performance of the TF-WD algorithm was evaluated by comparing its findings in 40 files with those annotated by two experts. Re-sults suggest that the TF-WD algorithm is an efficient and robust method for computerised wheeze detection in LRTI (SE=72.5%; SP=99.2%). Furthermore, sig
nificant correlations were found between the percentage predicted of forced expiratory volume in 1 second and forced vital capacity (FEV1pp and FVCpp) and wheeze duration at lateral (rs=-0.9, p=0.03) and posterior (rs=-0.9, p=0.01) right regions respectively. These results support the use of pulmonary auscultation and spirometry to detect areas of obstruction in LRTI.
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