Table 4.

Summary of several methods of crackle detection.

Methodology	Parameters	References
Time-frequency analysis	Gaussian band width, peak frequency, total deflection width, maximal deflection width	[46] (correct classification level: 87.78%)
Time-frequency analysis	Gaussian band width, peak frequency, maximal deflection width	[46] (correct classification level: 90.5% )
Prony modeling	Parameters of the Prony model	[46] [27] (correct classification level: 63.89%)
Wavelet transform	Autoregressive coefficients	[1] [64]
	Wavelet scale	[46] [27] (correct classification level: 93.9%)
	Wavelet transform fractal dimension based	[14] [46]
	Wavelet transform stationary – non stationary	[27]
Fuzzy rule-based system – FST-NST	27 fuzzy rules	[46]
Artificial neural networks	Autoregressive coefficients, wavelet coefficients, crackle parameters	[46] [27]
Empirical mode decomposition	Intrinsic mode function: local zero mean oscillating waves obtained by sifting process	[27]

FST-NST: stationary-non-stationary fuzzy-based filter.