In this paper, a Fuzzy Hidden Markov Model (FHMM) for electrocardiogram (ECG)‐based emotion recognition is proposed. The FHMM model is modeled using the fuzzy membership of each class of feature vectors to compute the elements of the matric in the model. Each element in the matric is determined by the two nearest classes of feature vectors with fuzzy classification to avoid the winner‐takes‐all situation that usually happens in tradition discrete Hidden Markov Models (HMM) and that reduces the accuracy of the modeling results. The FHMM modeling proposed in this paper can improve the precision of traditional discrete HMM modeling. Also, the features for emotion recognition, which are selected according to those used in prior research, are calculated from ECG signals. The selected features form a feature vector and these are calculated for all ECG signals. Some feature vectors are used to train the FHMM model and the remaining are used for testing. Experimental results show that the proposed FHMM model can achieve impressive improvements on the three indices, sensitivities (Se%), positive predictive value (PPV%), and total classification accuracy (TCA%), compared to traditional HMM. Moreover, compared to some existing studies, the recognition rates of the proposed method are higher. These results verify the efficiency of the proposed method for emotion recognition from ECG signals.

中文翻译：

---

使用心电图信号进行情绪检测的Fuzzy-HMM建模

本文提出了一种基于心电图（ECG）的情绪识别的模糊隐马尔可夫模型（FHMM）。使用每类特征向量的模糊隶属度对FHMM模型进行建模，以计算模型中的矩阵元素。矩阵中的每个元素都由具有模糊分类的两类最接近的特征向量确定，以避免传统的离散隐马尔可夫模型（HMM）中通常发生的赢家通吃的情况，并且降低了建模结果的准确性。本文提出的FHMM建模可以提高传统离散HMM建模的精度。同样，根据先前研究中使用的特征选择的用于情绪识别的特征是从ECG信号中计算出来的。所选特征形成一个特征向量，并针对所有ECG信号进行计算。一些特征向量用于训练FHMM模型，其余特征向量用于测试。实验结果表明，与传统的HMM相比，所提出的FHMM模型可以在灵敏度（Se％），阳性预测值（PPV％）和总分类准确度（TCA％）三个指标上实现令人印象深刻的改进。此外，与现有的一些研究相比，该方法的识别率更高。这些结果验证了从心电信号中识别情绪的方法的有效性。与传统HMM相比，阳性预测值（PPV％）和总分类准确度（TCA％）。而且，与现有的一些研究相比，该方法的识别率更高。这些结果验证了从心电信号中识别情绪的方法的有效性。与传统HMM相比，阳性预测值（PPV％）和总分类准确度（TCA％）。此外，与现有的一些研究相比，该方法的识别率更高。这些结果验证了从心电信号中识别情绪的方法的有效性。