Academic journal article Iranian Journal of Psychiatry

The Effect of Creative Tasks on Electrocardiogram: Using Linear and Nonlinear Features in Combination with Classification Approaches

Academic journal article Iranian Journal of Psychiatry

The Effect of Creative Tasks on Electrocardiogram: Using Linear and Nonlinear Features in Combination with Classification Approaches

Article excerpt

(ProQuest: ... denotes formulae omitted.)

^Nowadays, ECG is an important tool in many scientific fields (1). Han and Wolf discovered the relationship between the nervous and cardiovascular system in 1963 (2). As the heart muscle generates ECG signals, information from normal and pathological states of heart can be understood from the ECG signals (3, 4). Over the past few decades, many computational tools and methods for analyzing these signals have been proposed. These tools automatically analyze the electricity of the heart and reveal the cardiac anomalies. Many researchers are interested in detecting cardiac behavior like stress (5), anger and fear (6) or psychiatric diseases (7) by applying ECG signals processing .

Historic review on creativity suggests that in recent decades there has been great interest towards creativity education. In 1956, Torrance designed various types of tests to determine the creativity that people still used in many studies (8, 9).

Brainstorming is performed to nurture creativity exercises and in this technique fluency, flexibility and innovation are used (8-12).

Creative thinking is closely related to the nervous system, which consists of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The SNS activities increase heart rate and ECG (Nguyen, Zeng, 2014). Therefore, SNS becomes dominant and causes biological and psychological changes to adjust the body to the creative situation. On the other hand, the critical signals of the body help diagnose cognitive behavior. In 2006, Ghacibeh et al. (13) found that any stimulation of the vagus nerve (sympathetic system) is associated with raised heart rate, causing injury test and thus reducing the level of individual creativity. Previously, electroencephalography (EEG) signals were used in the detection of creativity states (14). Although these studies have been done statistically, detailed analysis has not been yet done based on feature extraction. In addition, the role of physiological signals such as ECG has not been studied. For the first time, in this paper, not only the creative effects on psychological parameters with ECG analysis is checked, but also an algorithm is suggested to classify creativity states.

The aim of this study was to separate different aspects of creativity with ECG signals. To this end, time domain and frequency domain characteristics of linear features and nonlinear features like Renyi's entropy and fractal dimension were extracted from ECG signals in rest and creativity states. Then all features were applied on two classifiers. Figure 1 demonstrates the research schematically.

Materials and Method

Data Collection

In this study, to assess the student's creative thinking, we used Torrance Test of Creative Thinking (TTCT) Form B (figural). The test of high discrimination for evaluating creativity has cognitive components, mainly in the form of assignments, practicable creativity and creative problem-solving techniques. The ECG signals were collected of 52 students of biomedical engineering, material engineering and control engineering (26 female and 26 male; 19-24 years). The participants were asked to get enough sleep and not to drink coffee for five hours. All tasks were explained to the participants before recording. They sat on comfortable chairs, and electrodes were connected to their wrists. They were present in the lab half-hour before. The observer had a conversation with the participant to relax them. ECG signals were recorded from lead II, 2-minutes of rest states and 30 minutes in creativity states, while the TTCT was done. Sampling frequency was 1000 Hz. ECG signals were recorded in the Computational Neuroscience Laboratory of Sahand University of Technology.

Pre-Processing

ECG recordings are combined with high-frequency noises such as crosstalk, Electromyography noise and other equipment (1). All signals were pre-processed with Notch filter 50Hz to remove power line noise. …

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