# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 1 Introduction
Although it is currently easier to acquire data, which can overcome the low cardinality of datasets, the application of machine learning techniques is still limited by inter-subject heterogeneity. Even in the same resting condition, without external stimuli, physiological signals appear to be significantly subject-dependent. In this work, the reduction of inter-subject heterogeneity was faced in the case of the heartbeat, mainly detected through Photoplethysmography (PPG) [24]. The PPG signal is one of the most-used signals to measure arousal [5,25] and, consequently, to detect stress and evaluate the experienced cognitive load [20,26]. The PPG signal of each subject appears different, both in terms of amplitude and beat frequency. Regarding the amplitude, differences can be due to the subjects' skin characteristics or to different sensor adherence during the acquisition phase. Concerning the diversity in terms of heartbeat, according to the American Heart Association, the heartbeat frequency of a resting adult can vary in the range between 60 and 100 beats per minute, and it depends on many different factors, both personal (such as age, sex, ethnicity, sports ability, diet, illnesses, prescribed medications, etc.) and environmental (humidity, temperature, etc.) [27]. Normalization procedures based on data rescaling are often used [28] to overcome amplitude variability within subjects. The main strategies adopted involve rescaling to the range \([0,1]\)[29], normalizing by dividing by the maximum value of the signal [30], and applying the Z-score [31]. None of these methods, however, take into account the effective differences in the subjects' heartbeat, which are not only related to amplitude, but also to frequency. The aim of this work was to solve this inter-subject variability, proposing a novel personalized PPG normalization based on the heartbeat of the subject in a resting state condition. To validate the normalization procedure presented here, PPG data belonging to two different datasets were considered: the Cognitive Load and Affective Walkability in Different Age Subjects(CLAWDAS) dataset, partially introduced in [32, 33, 34], and the Cognitive Load, Affect and Stress recognition (CLAS) dataset, available in [35]. The proposed normalization was analyzed considering a binary classification task to discriminate cognitive load in the relaxed state and compared with normalization strategies adopted in the literature. The paper is organized as follows. In Section 3, the two considered datasets are described, while the preliminary signal preprocessing strategies applied to each of them are reported in Section 4. The novel personalized PPG normalization strategy, based on the subject's resting state heartbeat, is presented in Section 5. Extracted features, classification strategies, and the adopted cross-validation approach are reported in Section 6. The comparison of the classification performances on the two datasets, obtained with different normalization strategies, is then reported and analyzed in Section 7. Finally, the conclusions are drawn in the last section.