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Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT)
[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HR...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Society of Physical Therapy Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3820413/ https://www.ncbi.nlm.nih.gov/pubmed/24259846 http://dx.doi.org/10.1589/jpts.25.753 |
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author | Murugappan, Murugappan Murugappan, Subbulakshmi Zheng, Bong Siao |
author_facet | Murugappan, Murugappan Murugappan, Subbulakshmi Zheng, Bong Siao |
author_sort | Murugappan, Murugappan |
collection | PubMed |
description | [Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems. |
format | Online Article Text |
id | pubmed-3820413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | The Society of Physical Therapy Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38204132013-11-20 Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT) Murugappan, Murugappan Murugappan, Subbulakshmi Zheng, Bong Siao J Phys Ther Sci Original [Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems. The Society of Physical Therapy Science 2013-08-20 2013-07 /pmc/articles/PMC3820413/ /pubmed/24259846 http://dx.doi.org/10.1589/jpts.25.753 Text en by the Society of Physical Therapy Science http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. |
spellingShingle | Original Murugappan, Murugappan Murugappan, Subbulakshmi Zheng, Bong Siao Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human
Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title_full | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human
Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title_fullStr | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human
Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title_full_unstemmed | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human
Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title_short | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human
Emotional State Classification Using Discrete Wavelet Transform (DWT) |
title_sort | frequency band analysis of electrocardiogram (ecg) signals for human
emotional state classification using discrete wavelet transform (dwt) |
topic | Original |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3820413/ https://www.ncbi.nlm.nih.gov/pubmed/24259846 http://dx.doi.org/10.1589/jpts.25.753 |
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