Ulukaya, SezerSerbes, GorkemSen, IpekKahya, Yasemin P.2024-06-122024-06-1220161557-170X1558-4615https://hdl.handle.net/20.500.14551/2500838th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) -- AUG 16-20, 2016 -- Orlando, FLI n this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.eninfo:eu-repo/semantics/closedAccessFrequencyConference Object37453748N/AWOS:0003998235040252-s2.0-8500909083928269104N/A