Feature Name | Definition | Description and references |
---|---|---|
Mean absolute value (MAV) | [25] | |
Variance (VAR) | [25] | |
Wave length (WL) | [25] | |
Wavelet denoise (WDEN) | θ = σ (0.3936 + 0.1829log_{2}(N)), σ-standard deviation of noise |
Symmlet 7 mother wavelet, and hard threshold (θ). [28, 29]. Finally the feature is MAV of denoised signal. |
Energy based on discrete Fourier transformation (DFT) |
, where X [k] is DFT of x [n] | [25] |
Autoregressive coefficients (AR) | The forward-backward approach. The sum of a least squares criterion for a forward model and the analogous criterion for a time-reversed model is minimized [27]. | |
Cepstral coefficients (CEPS) |
c_{1} = -a_{1} | The cepstrum coefficients (c_{i}), are calculated from AR coefficients (AR model with order P), as proposed in Kang's work [26]. |
Autocorrelation-based, second order processing (HOS2) |
, H_{0}-noise only (null hypotesis), H_{1}-presence of signal | Toepliz matrix creation, based on estimate of autocorrelation; singular value decomposition; difference among maximum and minimum eigenvalue (σ) [21]. |
Cumulant-based third order processing (HOS3) |
, H_{0}-noise only, H_{1}-presence of signal | Toepliz matrix creation, based on estimate of third order cumulant of a data frame; singular value decomposition; the largest eigenvalue (λ) [21]. |
Autocorrelation minimum value (ACORR) |
, L-length of ROW | First negative peak value of r(τ) [23, 24]. |