Li Ya, Tan Runxi, Lin Tianxin, Liu Qing, Wang Chang-Dong, Chen Min
IEEE J Biomed Health Inform. 2024 Sep;28(9):5201-5213. doi: 10.1109/JBHI.2024.3403188. Epub 2024 Sep 5.
In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95.08% in arousal and 95.90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.
近年来，基于心电图（ECG）信号识别人类情绪一直是研究人员关注的一个新领域。尽管从ECG信号中提取潜在情绪信息存在挑战，但现有方法能够通过计算心率变异性（HRV）特征来识别情绪。然而，这些局部特征存在缺点，因为它们不能全面描述ECG信号，导致识别性能欠佳。我们首次提出了一种从全局ECG轨迹中提取隐藏情绪信息以进行情绪识别的新策略。具体而言，通过总体经验模态分解（EEMD）将一段ECG信号分解为不同频段的子信号，并通过将这些子信号正交组合来构建一系列多序列轨迹图，以提取潜在情绪信息。此外，为了更好地利用这些图特征，设计了一个网络，该网络包括用于分类的自监督图表示学习和集成学习。这种方法超越了近期的显著成果，取得了优异的结果，在唤醒度检测中的准确率为95.08%，在效价检测中的准确率为95.90%。此外，将这种全局特征与HRV特征进行了比较和讨论，旨在为后续研究提供启发。
