2022/02/23 by Francesca Gasparini, Alessandra Agnese Grossi, Gasparini, Francesca +6 · 1 citation
Engineering · Medicine · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Heart Rate Variability and Autonomic Control #Non-Invasive Vital Sign Monitoring #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.11465
14 pages, 5 figures, 6 tables
arxiv created 2022/02/23 · openalex publication_date 2022/02/23 · arxiv updated 2022/02/24 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Physiological responses are nowadays 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 is considered in the case of PhotoPlethysmoGraphy (PPG), which is successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is here 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 is evaluated in comparison with other normalization procedures in a binary classification task, where cognitive load and relaxing state are considered. The results obtained on two different datasets available in the literature confirm that applying the proposed normalization strategy permits to increase classification performance.