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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/815
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dc.contributor.authorRai, Deepak-
dc.contributor.authorThakkar, Hiren Kumar-
dc.contributor.authorSingh, Deepak-
dc.date.accessioned2023-04-01T13:14:48Z-
dc.date.available2023-04-01T13:14:48Z-
dc.date.issued2020-12-
dc.identifier.isbn9781728169163-
dc.identifier.urihttp://lrcdrs.bennett.edu.in:80/handle/123456789/815-
dc.descriptionhttps://ieeexplore.ieee.org/xpl/conhome/9341296/proceedingen_US
dc.description.abstractRecently, Seismocardiogram (SCG) is used to monitoring vital cardiac health parameters through relevant SCG peaks annotation. However, accurate annotation of the SCG peaks is a challenging task. In this paper, we design binary classifiers to annotate the Isovolumic movement and the Aortic valve closure events of SCG. Five binary classifiers, such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Gaussian Nave Bayes, are implemented atop customized features Amplitude and Time of Appearance. The experiments are carried out on the cebs database available at Physionet public repository. In particular, seven rigorous performance metrics are employed to evaluate the classifiers' annotation ability, followed by the 5-fold cross-validation to ensure the classifiers' performance reliability. Experiment results show that RF consistently performs better for both IM and AC annotation. On the contrary, SVM and LR perform poorly for IM and AC, respectively. © 2020 IEEE.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectAnnotation; Aortic Valve Closure; Isovolumic Movement; Machine Learning; Seismocardiogramen_US
dc.titleMachine Learning Assisted Automatic Annotation of Isovolumic Movement and Aortic Valve Closure using Seismocardiogram Signalsen_US
dc.typeArticleen_US
dc.indexedscen_US
Appears in Collections:Conference Proceedings_ SCSET

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