This paper presents an automatic ECG-based emotion recognition algorithm for human emotion recognition. First, we adopt a musical induction method to induce participants’ real emotional states and collect their ECG signals without any deliberate laboratory setting. Afterward, we develop an automatic ECG-based emotion recognition algorithm to recognize human emotions elicited by listening to music. Physiological ECG features extracted from the time-, and frequency-domain, and nonlinear analyses of ECG signals are used to find emotion-relevant features and to correlate them with emotional states. Subsequently, we develop a sequential forward floating selection-kernel-based class separability-based (SFFS-KBCS-based) feature selection algorithm and utilize the generalized discriminant analysis (GDA) to effectively select significant ECG features associated with emotions and to reduce the dimensions of the selected features, respectively. Positive/negative valence, high/low arousal, and four types of emotions (joy, tension, sadness, and peacefulness) are recognized using least squares support vector machine (LS-SVM) recognizers. The results show that the correct classification rates for positive/negative valence, high/low arousal, and four types of emotion classification tasks are 82.78, 72.91, and 61.52 percent, respectively.

中文翻译：

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音乐聆听中基于心电图的自动情绪识别

本文提出了一种基于心电图的自动情感识别算法，用于人类情感识别。首先，我们采用音乐感应方法来诱导参与者的真实情绪状态并收集他们的心电图信号，而无需任何刻意的实验室设置。之后，我们开发了一种基于 ECG 的自动情绪识别算法来识别通过听音乐引起的人类情绪。从时域和频域中提取的生理 ECG 特征以及 ECG 信号的非线性分析用于找到与情绪相关的特征并将它们与情绪状态相关联。随后，我们开发了一种基于顺序前向浮动选择核的类可分性（SFFS-KBCS-based）特征选择算法，并利用广义判别分析（GDA）有效地选择与情绪相关的重要心电图特征并减少分别选定的特征。使用最小二乘支持向量机 (LS-SVM) 识别器识别正/负价、高/低唤醒和四种情绪（喜悦、紧张、悲伤和平静）。结果表明，正/负效价、高/低唤醒和四种情绪分类任务的正确分类率分别为82.78%、72.91%和61.52%。使用最小二乘支持向量机 (LS-SVM) 识别器识别四种类型的情绪（喜悦、紧张、悲伤和平静）。结果表明，正/负效价、高/低唤醒和四种情绪分类任务的正确分类率分别为82.78%、72.91%和61.52%。使用最小二乘支持向量机 (LS-SVM) 识别器识别四种类型的情绪（喜悦、紧张、悲伤和平静）。结果表明，正/负效价、高/低唤醒和四种情绪分类任务的正确分类率分别为82.78%、72.91%和61.52%。