Algorithm 1: Denoising

Step 1: $L_{coeff}=dwt\left(data,wavelet,mode\right)$ Step 2: $V_{sigma}=\left[\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$0.6745$}\right.\right)\times M_{a. dev}\left(L_{coeff}\right)\right]$ Step 3: $V_{thresh}=V_{sigma}\times \sqrt{(2\times log \left(len\left(data\right)\right)}$ Step 4: $F_{coeff}=\forall x_i\in L_{coeff}, \left\{ i=0, 1,2\dots n \right\}$ $\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} thresh\left(x_i,V_{thresh},mode\right)$ Step 5: $R_{sig}=Inverse\_dwt \left(F_{coeff},wavelet,mode\right)$