# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 8 Conclusions  
While considering physiological data, signal normalization not only with respect to amplitude, but also with respect to personal characteristics is mandatory to perform subject-based analysis, especially if machine learning techniques should be applied. Personalized normalization on PPG data, both at the feature level, SubjFeatN, and with respect to heartbeat frequency, PersFreqN, introduced an increase in the classification performance, considering different classification models and datasets. In particular, the personalized PPG normalization based on subject heartbeat herein proposed, PersFreqN, outperformed the other strategies and permitted significantly reducing inter-subject heterogeneity. Moreover, the proposed normalization could also be useful for intra-subject analysis, especially when comparing the physiological responses of the same subject, on different days or even at different moments of the day: it is well known, in fact, that the physiological responses not  
Figure 6: Bar plot comparison of the accuracy obtained using different classifiers and normalization procedures. Three datasets are considered: CLAS, CLAWDAS Young, and CLAWDAS Elderly.  
only depend on external stimuli, but also on physical and internal conditions, which can significantly vary for the same subject with respect to time.  
F.G.: conceptualization, methodology, formal analysis, investigation, data curation, writing--original draft preparation; A.G.: software, validation, formal analysis, investigation, data curation, writing--original draft preparation, visualization; M.G.: investigation, writing--review and editing; S.B.: project leader, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the FONDAZIONE CARIPLO \"LONGEVICITY-Social Inclusion for the Elderly through Walkability\" (Ref. 2017-0938) and by the Japan Society for the Promotion of Science (Ref. L19513).  
The experimental protocol for CLAWDAS dataset was reviewed and approved by the Research Ethics Committee at The University of Tokyo, Japan (Nos. 19-283 and 19-376).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
We want to give our thanks to Prof. [PERSON] and his staff, in particular [PERSON] and [PERSON], for their indispensable support during the experiment held at RCAST--The University of Tokyo.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PPG & Photoplethysmography \\ CLAS dataset & Cognitive Load, Affect and Stress Recognition dataset \\ CLAWDAS dataset & Cognitive Load and Affective Walkability in Different Age Subjects dataset \\ BL & Baseline \\ MP & Math Problem \\ ST & Stroop Test \\ LP & Logic Problem \\ NS & Neutral State \\ MC & Math Calculation \\ R & Reading \\ C & Comprehension \\ SWT & Stationary Wavelet Transform \\ CTD & Continuous Time Domain \\ DTD & Discrete Time Domain \\ SND & Subject Normalized discrete Domain \\ High CL & High Cognitive Load \\ Low CL & Low Cognitive Load \\ IBI & Inter-Beat Interval \\ RMSSD & Root Mean Square of Successive Distance \\ SVM & Support Vector Machine \\ SVM Linear & Support Vector Machine with Linear kernel \\ SVM Cubic & Support Vector Machine with polynomial Cubic kernel \\ SVM Gauss & Support Vector Machine with Gaussian kernel \\ CART & Classification and Regression Tree \\ LOSO cross-validation & Leave One Subject Out Cross-Validation \\ AmpN & Amplitude Normalization \\ SubjFeatN & Amplitude normalization followed by a Subject Feature Normalization \\ PersFreqN & Personalized Normalization based on baseline heartbeat Frequency \\ \end{tabular}