## 3 Combinatorial Fusion Approach (CFA)  
Combinatorial Fusion (CF) ([PERSON] et al. [50; 51; 52]) gives a useful piece of fusion method information in the analysis of a set of Multiple Scoring Systems (MSS) to obtain better accuracy and higher effectiveness of feature characteristics. Each system contains a score function, a rank function, and a Rank-Score Characteristic (RSC) function (see Figure 5). The scoring or ranking behavior is indicated by the RSC function and it is extremely easy to compute. Plus, the cognitive diversity of at least two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Number** & **Symbol** & **Name** & **Units** & **Feature Description** \\ \hline \hline \multirow{8}{*}{F} & \multicolumn{4}{c}{**Time domain**} \\ \cline{2-5}  & A & mean\_HRV & ms & Mean of all RR intervals. \\ \cline{2-5}  & B & SDNN & ms & Standard deviation of all NN intervals. \\ \cline{2-5}  & C & mean\_HR & \(1/\)min & Mean of all heart rate. \\ \cline{2-5}  & D & SD\_HR & \(1/\)min & Standard deviation of all heart rate. \\ \cline{2-5}  & E & RMSSD & ms & The square root of the mean of the sum of the squares of differences between adjacent NN intervals. \\ \cline{2-5}  & F & NN50 & count & Number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording. Three variants are possible counting all such NN interval pairs or only pairs in which the first or the second interval is longer. \\ \cline{2-5}  & G & pNN50 & \% & NN50 count divided by the total number of all MM intervals. \\ \cline{2-5}  & H & HRV triangular index & & Total number of all NN intervals divided by the height of the histogram of all NN intervals measured on a discrete scale with bins of 7 8125 ms (1/128 s). \\ \cline{2-5}  & I & TNN & ms & Baseline width of the minimum square difference triangular interpolation of the highest peak of the histogram of all NN intervals. \\ \cline{2-5}  & & & & **Frequency domain** \\ \cline{2-5}  & J & VLF\_peak & Hz & Peak of very low frequency. \\ \cline{2-5}  & K & LF\_peak & Hz & Peak of low frequency. \\ \cline{2-5}  & L & HF\_peak & Hz & Peak of high frequency. \\ \cline{2-5}  & M & VLF\_power & ms\({}^{2}\) & Power of very low frequency range. \\ \cline{2-5}  & N & VLF\_percentage & \% & Percentage power of very low frequency range. \\ \cline{2-5}  & O & LF\_power & ms\({}^{2}\) & Power of low frequency range. \\ \cline{2-5}  & P & LF\_percentage & \% & Percentage power of low frequency range. \\ \cline{2-5}  & Q & LF\_norm & n.u. & Low frequency power in normalized units. \\ \cline{2-5}  & R & HF\_power & ms\({}^{2}\) & Power of high frequency range. \\ \cline{2-5}  & S & HF\_percentage & \% & Percentage power of high frequency range. \\ \cline{2-5}  & T & HF\_norm & n.u. & High frequency power in normalized units. \\ \cline{2-5}  & U & LF\(/\)HF & ms\({}^{2}\) & Ratio LF (ms\({}^{2}\))/HF (ms\({}^{2}\)). \\ \cline{2-5}  & V & TP & ms\({}^{2}\) & Variance of all NN intervals. \\ \cline{2-5}  & W & EDR & Hz & Respiration frequency. \\ \hline \multirow{8}{*}{EMG Feature} & \multirow{8}{*}{9} & a & EMG\_mean & ms\({}^{2}\) & The normalized mean of the all EMG data. \\ \cline{2-5}  & b & EMG\_SD & ms\({}^{2}\) & Standard deviation of all EMG data. \\ \cline{1-1} \cline{2-5}  & & c & EMG\_RMS & ms\({}^{2}\) & Square root of the mean over time of the square of the vertical distance of the graph from the rest state. \\ \cline{1-1} \cline{2-5}  & d & EMG\_maxamp & ms\({}^{2}\) & Maximum amplitude of EMG. \\ \cline{1-1} \cline{2-5}  & e & EMG\_MDF & ms\({}^{2}\) & Median frequency of EMG. \\ \cline{1-1} \cline{2-5}  & f & EMG\_MNF & ms\({}^{2}\) & Mean frequency of EMG. \\ \cline{1-1} \cline{2-5}  & g & EMG\_var & ms\({}^{2}\) & Variance of EMG. \\ \cline{1-1} \cline{2-5}  & h & EMG\_skew & ms\({}^{2}\) & Skewness of EMG. \\ \cline{1-1} \cline{2-5}  & i & EMG\_kurt & ms\({}^{2}\) & Kurtosis of EMG. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the 32 extracted features. scoring systems can be measured [52].