Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.