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dc.contributor.authorMurugappan, Muthusamy, Dr.
dc.contributor.authorMurugappan, Subbulakshmi
dc.contributor.authorBong, Siao Zheng
dc.date.accessioned2014-05-23T09:10:10Z
dc.date.available2014-05-23T09:10:10Z
dc.date.issued2013-06
dc.identifier.citationJournal of Physical Therapy Science, vol. 25(7), 2013, pages 753-759en_US
dc.identifier.issn0915-5287
dc.identifier.urihttps://www.jstage.jst.go.jp/article/jpts/25/7/25_jpts-2012-446/_article
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34698
dc.descriptionLink to publisher's homepage at https://www.jstage.jst.go.jpen_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.isoenen_US
dc.publisherSociety of Physical Therapy Scienceen_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectHeart rate variabilityen_US
dc.subjectHuman emotionsen_US
dc.titleFrequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT)en_US
dc.typeArticleen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US
dc.contributor.urlsubbulakshmi@unimap.edu.myen_US
dc.contributor.urlwendy880806@gmail.comen_US


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