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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/4991
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dc.contributor.authorTripathi, Prashant Mani-
dc.contributor.authorKomaragiri, Rama S.-
dc.date.accessioned2024-06-13T08:04:34Z-
dc.date.available2024-06-13T08:04:34Z-
dc.date.issued2023-
dc.identifier.issn2666-9900-
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2023.107680-
dc.identifier.urihttp://lrcdrs.bennett.edu.in:80/handle/123456789/4991-
dc.description.abstractEpilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approxi mately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accu rately with minimal human intervention. Proposed approach: This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method. Methodology: SLT, a high-resolution time-frequency technique, converts EEG records into two dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems. Results: The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure de tection cases considered in this work. In the case of three and five-class classification problems, the pro posed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events. Conclusion: The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.en_US
dc.language.isoen_USen_US
dc.publisherComputer Methods and Programs in Biomedicineen_US
dc.subjectDeep learningen_US
dc.subjectElectroencephalogramen_US
dc.subjectSeizureen_US
dc.subjectSuperlet transformen_US
dc.titleAutomatic seizure detection and classification using super-resolution superlet transform and deep neural network -A preprocessing-less methoden_US
dc.typeArticleen_US
dc.indexedscen_US
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