Sahae Zakeri - M.Sc. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty ofElectrical Engineering, Sahand University of Technology, Tabriz, Iran
Ataollah Abbasi - Assistant professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty ofElectrical Engineering, Sahand University of Technology, Tabriz, Iran
Ateke Goshvarpour - Ph.D. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty ofElectrical Engineering, Sahand University of Technology, Tabriz, Iran

As Electrocardiogram (ECG) analysis is often used to detect cognitive behavior, this paper presents anovel approach for distinction between male/female and normal/creativity states from ECG signals. The goal of thisarticle is to indicate the heart mechanisms that mediate creativity, and how detect the creative men or womensubjects. For these purposes, a nonlinear feature of the ECG signal was extracted to detect creativity states. Doingthree tasks of Torrance Tests of Creative Thinking (TTCT- Figural B), ECG signals of 52 participants (26 men, 26women and 19-24 years) were recorded. Then, the performance of Support Vector Machine (SVM) classificationwas evaluated. The results showed that the best accuracy between male/female is 91.74% and normal/creativitystates is 91.36% with this classifier.

Creativity, Electrocardiogram, Gender, Fractal Dimession, Support Vector Machine