Zakeri Sahar, Abbasi Ataollah, Goshvarpour Ateke
M.Sc., Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Associate Professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Iran J Psychiatry. 2017 Jan;12(1):49-57.
Interest in the subject of creativity and its impacts on human life is growing extensively. However, only a few surveys pay attention to the relation between creativity and physiological changes. This paper presents a novel approach to distinguish between creativity states from electrocardiogram signals. Nineteen linear and nonlinear features of the cardiac signal were extracted to detect creativity states. ECG signals of 52 participants were recorded while doing three tasks of Torrance Tests of Creative Thinking (TTCT/ figural B). To remove artifacts, notch filter 50 Hz and Chebyshev II were applied. According to TTCT scores, participants were categorized into the high and low creativity groups: Participants with scores higher than 70 were assigned into the high creativity group and those with scores less than 30 were considered as low creativity group. Some linear and nonlinear features were extracted from the ECGs. Then, Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to classify the groups. Applying the Wilcoxon test, significant differences were observed between rest and each three tasks of creativity. However, better discrimination was performed between rest and the first task. In addition, there were no statistical differences between the second and third task of the test. The results indicated that the SVM effectively detects all the three tasks from the rest, particularly the task 1 and reached the maximum accuracy of 99.63% in the linear analysis. In addition, the high creative group was separated from the low creative group with the accuracy of 98.41%. : the combination of SVM classifier with linear features can be useful to show the relation between creativity and physiological changes.
人们对创造力及其对人类生活的影响这一主题的兴趣正在广泛增长。然而，只有少数调查关注创造力与生理变化之间的关系。本文提出了一种从心电图信号中区分创造力状态的新方法。提取了心脏信号的19个线性和非线性特征来检测创造力状态。在52名参与者进行托兰斯创造性思维测试（TTCT/图形B）的三项任务时记录了他们的心电图信号。为了去除伪迹，应用了50Hz的陷波滤波器和切比雪夫II型滤波器。根据TTCT分数，参与者被分为高创造力组和低创造力组：分数高于70的参与者被分配到高创造力组，分数低于30的参与者被视为低创造力组。从心电图中提取了一些线性和非线性特征。然后，使用支持向量机（SVM）和自适应神经模糊推理系统（ANFIS）对这些组进行分类。应用威尔科克森检验，在静息状态和每项创造力任务之间观察到了显著差异。然而，在静息状态和第一项任务之间的区分效果更好。此外，测试的第二项和第三项任务之间没有统计学差异。结果表明，SVM能够有效地从静息状态中检测出所有三项任务，特别是任务1，在线性分析中达到了99.63%的最大准确率。此外，高创造力组与低创造力组的区分准确率为98.41%。支持向量机分类器与线性特征的结合有助于揭示创造力与生理变化之间的关系。
