@inproceedings{emmanouilidou2012a,
author = {Emmanouilidou, Dimitra},
title = {A multiresolution analysis for detection of abnormal lung sounds},
booktitle = {International Conference of the IEEE Engineering in Medicine and Biology Society},
year = {2012},
month = {August},
abstract = {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 pediatric 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.2012.A multiresolution analysis for detection of abnormal lung sounds},
publisher = {IEEE Xplore},
url = {http://approjects.co.za/?big=en-us/research/publication/multiresolution-analysis-detection-abnormal-lung-sounds/},
pages = {3139-3142},
}