Frustration Detection with Electrocardiograph Signal Using Wavelet Transform

This paper proposes a computer aided system that aims to recognize frustration/mental fatigue in a student/individual and thereby be able to predict the student's learning/cognition ability. The objective is to recognize the existence of frustration based on the subjects ECG data. Discrete Wave...

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Bibliographic Details
Main Authors: Belle, Ashwin, Soo-Yeon Ji, Ansari, Sardar, Hakimzadeh, Roya, Ward, Kevin, Najarian, Kayvan
Format: Conference Proceeding
Language:English
Subjects:
Cognition
cognitive/learning ability
Data mining
Discrete wavelet transforms
Electrocardiography
Entropy
Fatigue
Feature extraction
frustration/fatigue(mental)
Heart rate variability
HRV
R-R interval
Signal detection
wavelet transform
Wavelet transforms
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Summary:This paper proposes a computer aided system that aims to recognize frustration/mental fatigue in a student/individual and thereby be able to predict the student's learning/cognition ability. The objective is to recognize the existence of frustration based on the subjects ECG data. Discrete Wavelet Transform (DWT) is applied on an individual's ECG data as well as on the Heart Rate Variability (HRV) to extract features such as standard deviation, median, entropy and energy. In this experiment, a mental workload is given to 4 subjects who are subjected to a series of test which targets the cognitive ability of the individual. Based on the analysis of the ECG collected efforts are focused towards assessing the existence of the frustration/mental fatigue and its correlation with subject's cognition ability. The results of the two methods applied in this paper show that it is possible to distinguish between an ECG sample when the individual is calm and an ECG sample when the individual is frustrated.
DOI:10.1109/BioSciencesWorld.2010.19