A fuzzy rule-based system for real-time separation of crackles from vesicular sounds
An automated way of revealing the diagnostic character of crackle by isolating them from vesicular sounds (VS) is presented in this paper. The proposed algorithm takes into account crackle nonstationarity and uses fuzzy rules in order to compose a fuzzy-based stationary-nonstationary filter (FST-NST). Applying the FST-NST filter to fine/coarse crackles, selected from three lung sound databases, the coherent structure of crackles is revealed and they are separated from VS. The resulted separation is accurate, objective, and of a high quality, since the FTST-NST filter automatically identifies the true location of crackles in the original signal and maintains their structure in its nonstationary output. Due to its simple and fast implementation it can easily be used as an on-line crackle identification system in clinical medicine.