# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 6 Classification Setting  
A binary classification task is herein proposed on CLAS, CLAWDAS Young, and CLAWDAS Elderly, to evaluate the performance of the personalized PPG normalization presented in this work. In particular, a cognitive load recognition task was considered in the analysis. We selected this task of classification because it is widely analyzed in the physiological signals' literature with also positive performance [21; 22]. Two classes were thus considered: the class corresponding to signals collected during high cognitive load  
Figure 4: PPG data in resting state conditions for Subjects 11 (**left**) and 18 (**right**) are herein reported, in the original discrete time domain (DTD) and in the Subject Normalized Domain (SND) (**top** and **bottom** rows, respectively). Before the proposed normalization (**top** row), the subjects have different heartbeat frequencies, while, in the SND, they are more similar. tasks: High CL, and the class related to low cognitive load tasks: Low CL. The tasks used as representative of each class changed according to the dataset considered. In particular, according to [35], in the CLAS dataset, the PPG data collected during the three cognitive tasks (MP, ST, and LP) were labeled as High CL, while the NS data were labeled as Low CL. In order to make the two classes balanced, MP, ST, and LP signals were split into two non-overlapped segments of equal length, reaching a cardinality of 360 instances for both classes. In CLAWDAS Young and CLAWDAS Elderly, the data collected during the MC task were selected for the High CL class, while the data collected during the AL task were chosen as representatives of the Low CL class. Thereby, the two classes were equally balanced, with 96 instances each for CLAWDAS Young and 120 instances each for CLAWDAS Elderly. In this study, the Math Calculation task was preferred over the Reading and Comprehension tasks as the High CL class because it is able to elicit high mental stress according to what has been defined in the literature [17]. In all the performed analysis, seven handcrafted features were extracted as characteristics useful to describe the PPG signals:  
* Minima, Maxima, Mean, and Standard Deviation of the signal;
* Peak Rate, which represents the mean number of peaks;
* Inter-Beat Interval (IBI), which represents the mean distance between two peaks in a row;
* Root Mean Square of Successive Distance (RMSSD), which represents the variance of the distance between two consecutive peaks [65]. The last three features were evaluated in the discrete domain and reported with respect to samples. For the sake of clarity, it is recalled that the meaning of samples changes according to the type of normalization strategy adopted. In particular, samples refer to subject normalized samples when the features are evaluated on signals with personalized normalization based on resting state heartbeat, while this term refers to discrete time samples in all the other cases. All the features so evaluated were also standardized by applying the z-score before being used as the input to the different classifiers. Using the the seven features introduced above, three binary classification experiments were performed for each of the two datasets considered, comparing the following three normalization strategies:  
* AmpN: Amplitude Normalization, as described in Section 4.2;
* SubjFeatN: amplitude normalization followed by a Subject Feature Normalization.