Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst
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BioMed Central
2014
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my.unimap-346112014-05-22T04:03:37Z Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst Selvaraj, Jerritta Murugappan, M., Dr. Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. sn.jerritta@gmail.com murugappan@unimap.edu.my khairunizam@unimap.edu.my s.yaacob@unimap.edu.my Audio-visual stimulus Autonomous nervous systems Classification accuracy Computer based training Electrocardiogram signal Link to publisher's homepage at http://www.biomedical-engineering-online.com/ 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. 2014-05-22T04:03:37Z 2014-05-22T04:03:37Z 2013-05 Article BioMedical Engineering Online, vol. 12(1), 2013, pages 1-18 1475-925X http://www.biomedical-engineering-online.com/content/12/1/44 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34611 10.1186/1475-925X-12-44 en BioMed Central |
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Universiti Malaysia Perlis |
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English |
| topic |
Audio-visual stimulus Autonomous nervous systems Classification accuracy Computer based training Electrocardiogram signal |
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Audio-visual stimulus Autonomous nervous systems Classification accuracy Computer based training Electrocardiogram signal Selvaraj, Jerritta Murugappan, M., Dr. Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
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Link to publisher's homepage at http://www.biomedical-engineering-online.com/ |
| author2 |
sn.jerritta@gmail.com |
| author_facet |
sn.jerritta@gmail.com Selvaraj, Jerritta Murugappan, M., Dr. Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. |
| format |
Article |
| author |
Selvaraj, Jerritta Murugappan, M., Dr. Wan Khairunizam, Wan Ahmad, Dr. Sazali, Yaacob, Prof. Dr. |
| author_sort |
Selvaraj, Jerritta |
| title |
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
| title_short |
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
| title_full |
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
| title_fullStr |
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
| title_full_unstemmed |
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst |
| title_sort |
classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst |
| publisher |
BioMed Central |
| publishDate |
2014 |
| url |
http://dspace.unimap.edu.my:80/dspace/handle/123456789/34611 |
| _version_ |
1643797542060687360 |
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13.222552 |