# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 2 State-of-the-Art  
In the last decade, the number of publications and citations related to heartbeat signals collected by photoplethysmography has widely increased [36]. In particular, the growing popularity of cheaper and non-invasive wearable devices has allowed extending the analysis of such signals from health applications to other areas of interest such as entertainment [25, 37], driving monitoring [38, 39], or emotional analysis [40, 41]. In many of these applications, the pipeline used to pre-process the PPG signals is similar and includes noise and motion artifact removal, as well as signal de-trending [42]. In [43], the PPG signals were pre-processed using a two-step procedure: first, each signal was filtered using a three-order low-pass Bessel filter and a notch filter to remove basic noise and baseline shift, then a direct comparison between PPG and ECG signals was performed to detect and eliminate the motion artifacts. Similarly, in [44], a bandpass filter was used to remove power line interference and motion artifacts and de-trend the signals, while a visual analysis was performed to remove the high peaks of noise. Other PPG denoising strategies, including methods based on empirical mode decomposition or wavelet decomposition, were summarized in [45]. In several literature works, the denoising and de-trending steps were followed by an amplitude normalization step performed to take into account subjects' heterogeneity. The amplitude of the PPG signals varies from person to person, and it is both influenced by subjective and environmental factors [45, 46].  
Many strategies of data rescaling have been proposed in the literature to normalize the signals of each person in a similar range of values [47]. A min-max normalization was applied to the filtered signal of each participant to rescale the heart rate data in the range \([0,1]\) in [29, 48, 49]. Similarly, in [5], the amplitude of each subject's PPG signal was rescaled in the range \([0,1000]\) using a min-max normalization strategy followed by the multiplication by a constant factor \(\alpha=1000\). Another normalization strategy usually applied in the literature is the Z-normalization or Z-score normalization. In this method, the amplitude of each subject's signal is standardized using the formula \(X_{n}=(X_{i}-\mu)/\sigma\), where \(\mu\) and \(\sigma\) are, respectively, the mean and variance of the analyzed PPG signal. This method was applied in [50] as the last step of the pre-processing phase, while in [51], it was applied as an intermediate operation between the denoising step and the motion artifact removal step. Finally, in [19, 52], an amplitude normalization based on the subject's baseline was considered using the formula \((X_{i}-\mu_{b})/max|X_{i}-\mu_{b}|\), where \(X_{i}\) is the PPG signal, \(\mu_{b}\) is the mean baseline value, and \(max|X_{i}-\mu_{b}|\) is the maximum range of each subject.  
While the amplitude normalization is usually considered in the analysis of PPG signals, the definition of a normalization strategy that considers the differences in the subjects' heartbeat from a frequency point of view is still an open area of research. In [5], the PPG signals of each subject were segmented into single-peak frames, and each frame was used as a separate signal in the analysis. This method allows solving the issue of subject's heartbeat heterogeneity, but restricts the features involved in the analysis to only peak morphological characteristics. In the following sections, our novel personalized PPG normalization based on the heartbeat of the subject in a resting state condition is introduced and described. In particular, a cognitive load classification task is considered in the analysis. We selected this task because it is widely analyzed in the literature, with positive results in terms of accuracy, making the validation of our proposal easier.