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Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

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 u...

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Autores principales: Selvaraj, Jerritta, Murugappan, Murugappan, Wan, Khairunizam, Yaacob, Sazali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3680185/
https://www.ncbi.nlm.nih.gov/pubmed/23680041
http://dx.doi.org/10.1186/1475-925X-12-44
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author Selvaraj, Jerritta
Murugappan, Murugappan
Wan, Khairunizam
Yaacob, Sazali
author_facet Selvaraj, Jerritta
Murugappan, Murugappan
Wan, Khairunizam
Yaacob, Sazali
author_sort Selvaraj, Jerritta
collection PubMed
description 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.
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spelling pubmed-36801852013-06-25 Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst Selvaraj, Jerritta Murugappan, Murugappan Wan, Khairunizam Yaacob, Sazali Biomed Eng Online Research 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. BioMed Central 2013-05-16 /pmc/articles/PMC3680185/ /pubmed/23680041 http://dx.doi.org/10.1186/1475-925X-12-44 Text en Copyright © 2013 Selvaraj et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Selvaraj, Jerritta
Murugappan, Murugappan
Wan, Khairunizam
Yaacob, Sazali
Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst
title 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_short 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
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3680185/
https://www.ncbi.nlm.nih.gov/pubmed/23680041
http://dx.doi.org/10.1186/1475-925X-12-44
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