A multiresolution analysis for detection of abnormal lung sounds.
Automated analysis and detection of abnormal lung sound patterns has great potential for improving access to standardized diagnosis of pulmonary diseases, especially in low-resource settings. In the current study, we develop signal processing tools for analysis of paediatric auscultations recorded under non-ideal noisy conditions. The proposed model is based on a biomimetic multi-resolution analysis of the spectro-temporal modulation details in lung sounds. The methodology provides a detailed description of joint spectral and temporal variations in the signal and proves to be more robust than frequency-based techniques in distinguishing crackles and wheezes from normal breathing sounds.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Dimitra
D
Emmanouilidou
Kailash
K
Patil
James
J
West
Mounya
M
Elhilali
D000465:Algorithms; D006801:Humans; D012135:Respiratory Sounds / Q000502:physiology*