论文标题
基于主题心跳在静止状态状态下的个性化PPG标准化
Personalized PPG Normalization based on Subject Heartbeat in Resting State Condition
论文作者
论文摘要
如今，生理反应被广泛用于识别现实生活中受试者的情感状态。但是，这些数据本质上依赖于受试者，因此由于受试者间的可变性，用于数据分类的机器学习技术不容易适用。在这项工作中，在光摄影学（PPG）的情况下考虑了受试者间异质性的降低，该杂志学（PPG）成功地用于检测压力并评估经历的认知载荷。为了面对受试者间的异质性，这里提出了一种新型的个性化PPG归一化。考虑到受试者在静止状态条件下受试者的心跳频率，引入了PPG信号的受试者归一化离散域。在考虑认知负荷和放松状态的二进制分类任务中，评估了所提出的归一化的有效性。在文献中可用的两个不同数据集上获得的结果证实，应用所提出的标准化策略允许提高分类性能。
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.
