@inproceedings{emmanouilidou2012EMBS,
<br>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 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.},
<br>author = {Emmanouilidou, D. and Patil, K. and West, J. and Elhilali, M.},
<br>booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
<br>doi = {10.1109/EMBC.2012.6346630},
<br>isbn = {978-1-4577-1787-1},
<br>issn = {1557170X},
<br>pages = {3139--3142},
<br>title = {{A multiresolution analysis for detection of abnormal lung sounds}},
<br>url = {http://ieeexplore.ieee.org/document/6346630/},
<br>volume = {2012},
<br>year = {2012}
<br>}