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Investigating the effect of sound in horror clip on the cardiac electrophysiology of young adults using wavelet packet decomposition and machine learning classifiers

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cris.virtual.author-orcid0000-0001-9832-9274
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cris.virtualsource.author-orcid36173a57-3417-4bf8-99ea-e027717d422c
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dc.abstract.enThe effect of horror sound on electro-cardiac activity is not yet sufficiently explored despite recommendable research on different genres of music and sound. Detecting the impact of these stimuli is a complex task. The current study intends to automatically detect the horror-sound induced cardiac changes using wavelet coefficient-based features extracted from the electrocardiograph signals and several machine learning models. The statistical comparison of these features in the two data groups suggests a significant change in different feature values after exposure to horror sound while watching a horror clip. The statistically discriminative features were employed as input to nine machine learning (ML) models, namely Naïve Bayes, Fast large margin, Logistic regression, generalized linear model, Decision tree, Random Forest, deep learning, gradient boosted tree, and Support vector machine. The performance of these ML models was compared using different performance measures, including accuracy, precision, F-measure, sensitivity, specificity, and area under the curve (AUC). The results suggest the gradient boosted tree is the best performing model in differentiating the horror sound-induced changes in the ECG signals.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliation.instituteKatedra Fizyki i Biofizyki
dc.contributor.authorPradhan, Bikash K.
dc.contributor.authorMishra, Chinmaya R.
dc.contributor.authorJarzębski, Maciej
dc.contributor.authorSivaraman, J
dc.contributor.authorRay, Sirsendu S.
dc.contributor.authorMohanty, Satyapriya
dc.contributor.authorPal, Kunal
dc.date.access2026-01-30
dc.date.accessioned2026-02-10T09:02:00Z
dc.date.available2026-02-10T09:02:00Z
dc.date.issued2022
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.numberJune 2022
dc.description.points5
dc.description.versionfinal_published
dc.description.volume3
dc.identifier.doi10.1016/j.bea.2022.100037
dc.identifier.issn2667-0992
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7278
dc.identifier.weblinkhttps://www.sciencedirect.com/science/article/pii/S2667099222000135
dc.languageen
dc.relation.ispartofBiomedical Engineering Advances
dc.relation.pagesart. 100037
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enECG
dc.subject.enhorror sound
dc.subject.enmachine learning
dc.subject.enfrequency bands
dc.subject.enwavelet packet decomposition
dc.titleInvestigating the effect of sound in horror clip on the cardiac electrophysiology of young adults using wavelet packet decomposition and machine learning classifiers
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.volume3