Goshvarpour Ateke, Abbasi Ataollah, Goshvarpour Atefeh
Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, New Sahand Town, PO. BOX 51335/1996, Tabriz, Iran.
Australas Phys Eng Sci Med. 2017 Jun;40(2):277-287. doi: 10.1007/s13246-017-0530-x. Epub 2017 Feb 16.
Interest in human emotion recognition, regarding physiological signals, has recently risen. In this study, an efficient emotion recognition system, based on geometrical analysis of autonomic nervous system signals, is presented. The electrocardiogram recordings of 47 college students were obtained during rest condition and affective visual stimuli. Pictures with four emotional contents, including happiness, peacefulness, sadness, and fear were selected. Then, ten lags of Poincare plot were constructed for heart rate variability (HRV) segments. For each lag, five geometrical indices were extracted. Next, these features were fed into an automatic classification system for the recognition of the four affective states and rest condition. The results showed that the Poincare plots have different shapes for different lags, as well as for different affective states. Considering higher lags, the greatest increment in SD and decrements in SD occurred during the happiness stimuli. In contrast, the minimum changes in the Poincare measures were perceived during the fear inducements. Therefore, the HRV geometrical shapes and dynamics were altered by the positive and negative values of valence-based emotion dimension. Using a probabilistic neural network, a maximum recognition rate of 97.45% was attained. Applying the proposed methodology based on lagged Poincare indices, a valuable tool for discriminating the emotional states was provided.
最近，人们对基于生理信号的人类情绪识别的兴趣有所增加。在本研究中，提出了一种基于自主神经系统信号几何分析的高效情绪识别系统。在静息状态和情感视觉刺激期间获取了47名大学生的心电图记录。选择了包含快乐、平静、悲伤和恐惧这四种情感内容的图片。然后，针对心率变异性（HRV）片段构建了庞加莱图的十个滞后值。对于每个滞后值，提取了五个几何指标。接下来，将这些特征输入到一个自动分类系统中，以识别这四种情感状态和静息状态。结果表明，庞加莱图对于不同的滞后值以及不同的情感状态具有不同的形状。考虑更高的滞后值，在快乐刺激期间，标准差（SD）出现了最大增幅，而在恐惧诱导期间，庞加莱测量值的变化最小。因此，基于效价的情绪维度的正负值改变了HRV的几何形状和动态变化。使用概率神经网络，获得了97.45%的最高识别率。应用基于滞后庞加莱指数的所提出方法，提供了一种用于区分情绪状态的有价值工具。
