# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 7 Results and Discussion  
The three different types of normalization, AmpN, SubjFeatN, and PersFreqN, are compared in this section, considering the high class and low class cognitive load binary classification task for the two chosen datasets. The performance corresponding to each of the classification settings described in Table 3 are reported in Tables 4-6, for CLAS, CLAWDAS Young, and CLAWDAS Elderly, respectively. In particular, the results of the different normalization strategies are reported in terms of accuracy and single-class F1-score generated using the LOSO cross-validation approach. Figure 5: Pre-processing and feature extraction pipeline applied to the signals of the two datasets. The operations performed change according to the experiment considered: Amplitude Normalization (AmpN), amplitude normalization followed by a Subject Feature Normalization (SubjFeatN), and amplitude normalization followed by the Personalized Normalization based on resting state heartbeat (PersFreqN). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Types of Normalization Considered** & AmpN, SubjFeatN, PersFreqN \\ \hline
**Features Used** & Maximum, Minimum, Mean, Standard Deviation, Peak Rate, IBI, RMSSD \\ \hline
**Classifier Involved** & SVM Linear, SVM Cubic, SVM Gauss, and CART \\ \hline
**Performance Evaluation Method** & LOSO \\ \hline
**Evaluation Metrics** & Accuracy, single-class F1-score \\ \hline
**Dataset** & **CL High Class** & **CL Low Class** \\  & **Task** & **Num. of signals** \\ \hline CLAS & Math, Stroop, and Logic Test & 360 \\ CLAWDAS Young & Math Calculation & 96 \\ CLAWDAS Elderly & Math Calculation & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the classification settings. Note that, despite the considered classifier, SVM Linear, SVM Cubic, SVM Gauss, and CART, in all the experiments carried out, the normalization strategy herein proposed, PersFreqN, outperformed the other two normalization procedures, for all the datasets. This observation is further supported by a visual comparison of the performance of the classifiers reported in terms of accuracy in the bar plot of Figure 6, varying the normalization strategy and the involved dataset. In particular, PersFreqN achieved the best accuracy for all the dataset and type of classifier, with the sole exception of the CART classifier for the CLAWDAS Young dataset, where the best performance was achieved by the SubjFeatN strategy. On the other hand, the AmpN normalization was in general the worst approach, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AmpN**} & \multicolumn{3}{c}{**SubjFeatN**} & \multicolumn{3}{c}{**PersFreqN**} \\ \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Acc**} & **High CL** & **Low CL** & & **High CL** & **Low CL** & & **High CL** & **Low CL** \\  & & **F1-Score** & **F1-Score** & **Acc** & **F1-Score** & **F1-Score** & **Acc** & **F1-Score** & **Acc** & **F1-Score** \\ \hline SVM Linear & 59\% & 0.58 & 0.60 & 69\% & 0.65 & 0.72 & **80\%** & 0.80 & 0.81 \\ SVM Cubic & 59\% & 0.55 & 0.62 & 64\% & 0.61 & 0.66 & 72\% & 0.71 & 0.73 \\ SVM Gauss & 63\% & 0.61 & 0.64 & 75\% & 0.74 & 0.76 & 78\% & 0.78 & 0.78 \\ CART & 54\% & 0.51 & 0.57 & 68\% & 0.68 & 0.67 & 75\% & 0.75 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison on CLAWDAS Elderly, varying the normalization strategies (columns) and classification models (rows). Two performance metrics are evaluated using a LOSO validation strategy: Accuracy (Acc) and single-class F1-score. The best performances reached for each type of normalization are underlined, while the the highest accuracy value of all is highlighted in bold.