title: Classification of emotional states from electrocardiogram signals: a non-linear approach based on Hurst.
abstract: BACKGROUND
  Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.
  
  
  METHODS
  Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.
  
  
  RESULTS
  Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.
  
  
  CONCLUSIONS
  The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.
authors.affiliation: Intelligent Signal Processing Research Cluster, School of Mechatronic Engineering, Universiti Malaysia Perlis-UniMAP, Kampus Ulu Pauh, Arau, Perlis 02600, Malaysia. sn.jerritta@gmail.com
authors.forename: Jerritta
authors.initials: J
authors.lastname: Selvaraj
authors.forename: Murugappan
authors.initials: M
authors.lastname: Murugappan
authors.forename: Khairunizam
authors.initials: K
authors.lastname: Wan
authors.forename: Sazali
authors.initials: S
authors.lastname: Yaacob
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D004562:Electrocardiography / Q000379:methods*; D004644:Emotions; D005260:Female; D006801:Humans; D008875:Middle Aged; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D055815:Young Adult