Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.
Sensors (Basel). 2023 Oct 22;23(20):8636. doi: 10.3390/s23208636.
Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system's influence on heart function but also unveils the connection between emotions and psychological disorders. Currently, in the field of emotion recognition using HRV, most methods focus on feature extraction through the comprehensive analysis of signal characteristics; however, these methods lack in-depth analysis of the local features in the HRV signal and cannot fully utilize the information of the HRV signal. Therefore, we propose the HRV Emotion Recognition (HER) method, utilizing the amplitude level quantization (ALQ) technique for feature extraction. First, we employ the emotion quantification analysis (EQA) technique to impartially assess the semantic resemblance of emotions within the domain of emotional arousal. Then, we use the ALQ method to extract rich local information features by analyzing the local information in each frequency range of the HRV signal. Finally, the extracted features are classified using a logistic regression (LR) classification algorithm, which can achieve efficient and accurate emotion recognition. According to the experiment findings, the approach surpasses existing techniques in emotion recognition accuracy, achieving an average accuracy rate of 84.3%. Therefore, the HER method proposed in this paper can effectively utilize the local features in HRV signals to achieve efficient and accurate emotion recognition. This will provide strong support for emotion research in psychology, medicine, and other fields.
心率变异性（HRV）是一种重要的生理指标，反映了心脏自主神经系统的调节能力。它不仅表明自主神经系统对心脏功能的影响程度，还揭示了情绪和心理障碍之间的联系。目前，在使用 HRV 进行情绪识别的领域中，大多数方法都侧重于通过全面分析信号特征进行特征提取；然而，这些方法缺乏对 HRV 信号中局部特征的深入分析，无法充分利用 HRV 信号的信息。因此，我们提出了 HRV 情绪识别（HER）方法，利用幅度水平量化（ALQ）技术进行特征提取。首先，我们采用情绪量化分析（EQA）技术，客观地评估情感唤起领域内情绪之间的语义相似性。然后，我们使用 ALQ 方法通过分析 HRV 信号每个频率范围内的局部信息来提取丰富的局部信息特征。最后，使用逻辑回归（LR）分类算法对提取的特征进行分类，从而实现高效准确的情绪识别。根据实验结果，该方法在情绪识别准确性方面优于现有技术，平均准确率达到 84.3%。因此，本文提出的 HER 方法可以有效地利用 HRV 信号中的局部特征，实现高效准确的情绪识别。这将为心理学、医学等领域的情绪研究提供有力支持。
