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dc.contributor.authorSelvaraj, Jerritta
dc.contributor.authorMurugappan, M., Dr.
dc.contributor.authorWan Khairunizam, Wan Ahmad, Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.date.accessioned2014-05-22T04:48:12Z
dc.date.available2014-05-22T04:48:12Z
dc.date.issued2013-09
dc.identifier.citationp. 849-854en_US
dc.identifier.isbn978-076955048-0
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6681551
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34629
dc.descriptionProceeding of The 5th Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013) at Geneva, Switzerland on 2 September 2013 through 5 September 2013. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jspen_US
dc.description.abstractEmotion recognition using physiological signals is one of the key research areas in Human Computer Interaction (HCI). In this work, we identify the six basic emotional states (Happiness, sadness, fear, surprise, disgust and neutral) from the QRS complex of electrocardiogram (ECG) signals. We focus specifically on the nonlinear feature 'Hurst exponent' computed using two methods namely rescaled range statistics (RRS) and finite variance scaling (FVS). The study is done on emotional ECG data obtained using audio visual stimuli from sixty subjects belonging to three different age groups - children (9 to 16 years), young adults (18 to 25 years) and adults (39 to 68 years). The performance of the Hurst exponent computed using RRS and FVS for individual age groups resulted in a maximum average accuracy of 78.21%. The combined analysis of the all the age groups had a maximum average accuracy of 70.23%. In general, the results of all the six emotional states indicate better performance compared to previous research works. However, the performance needs to be further improved in order to develop a reliable and robust emotion recognition system.en_US
dc.language.isoenen_US
dc.publisherIEEE Conference Publicationsen_US
dc.relation.ispartofseriesProceeding of The 5th Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013);
dc.subjectEmotionen_US
dc.subjectInducement Stimulien_US
dc.subjectPhysiological signalsen_US
dc.subjectSignal Processing Techniquesen_US
dc.titleEmotion detection from QRS complex of ECG signals using hurst exponent for different age groupsen_US
dc.typeWorking Paperen_US
dc.identifier.urlhttp://dx.doi.org/10.1109/ACII.2013.159
dc.contributor.urlsn.jerritta@gmail.comen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US
dc.contributor.urlkhairunizam@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US


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