dc.contributor.author | Murugappan, Muthusamy, Dr. | |
dc.contributor.author | Murugappan, Subbulakshmi | |
dc.contributor.author | Bong, Siao Zheng | |
dc.date.accessioned | 2014-05-23T09:10:10Z | |
dc.date.available | 2014-05-23T09:10:10Z | |
dc.date.issued | 2013-06 | |
dc.identifier.citation | Journal of Physical Therapy Science, vol. 25(7), 2013, pages 753-759 | en_US |
dc.identifier.issn | 0915-5287 | |
dc.identifier.uri | https://www.jstage.jst.go.jp/article/jpts/25/7/25_jpts-2012-446/_article | |
dc.identifier.uri | http://dspace.unimap.edu.my:80/dspace/handle/123456789/34698 | |
dc.description | Link to publisher's homepage at https://www.jstage.jst.go.jp | en_US |
dc.description.abstract | [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 Coifet5 (coif5). The k-nearest neighbor (KNN) and linear discriminate 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 - 88.6%; 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Society of Physical Therapy Science | en_US |
dc.subject | Discrete wavelet transform | en_US |
dc.subject | Heart rate variability | en_US |
dc.subject | Human emotions | en_US |
dc.title | Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT) | en_US |
dc.type | Article | en_US |
dc.contributor.url | murugappan@unimap.edu.my | en_US |
dc.contributor.url | subbulakshmi@unimap.edu.my | en_US |
dc.contributor.url | wendy880806@gmail.com | en_US |