TY - GEN
T1 - Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection
AU - Serbes, Gorkem
AU - Sakar, C. Okan
AU - Kahya, Yasemin P.
AU - Aydin, Nizamettin
PY - 2011
Y1 - 2011
N2 - Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed.
AB - Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed.
UR - http://www.scopus.com/inward/record.url?scp=84055222184&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2011.6090899
DO - 10.1109/IEMBS.2011.6090899
M3 - Conference contribution
C2 - 22255048
AN - SCOPUS:84055222184
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3314
EP - 3317
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -