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dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorHussain, Syed
dc.date.accessioned2022-11-03T11:57:25Z
dc.date.available2022-11-03T11:57:25Z
dc.date.issued2022
dc.identifier.citationLi, Yang & Wang, Xudong & Luo, Lin & Li, Ke & Yang, Xiao-Feng & Guo, Qi. (2017). Epileptic Seizure Classification of EEGs Using Time-Frequency Analysis Based Multiscale Radial Basis Functions. IEEE Journal of Biomedical and Health Informatics. PP. 1-1. 10.1109/JBHI.2017.2654479.
dc.identifier.isbn0957-4174
dc.identifier.doi10.1016/j.eswa.2021.116356
dc.identifier.urihttp://hdl.handle.net/20.500.14131/169
dc.description.abstractEpilepsy is a disorder of the brain characterized by seizures and requires constant monitoring particularly in serious patients. Electroencephalogram (EEG) signals are frequently used in epilepsy diagnosis and monitoring. A new paradigm of battery packed wearable gadgets has recently gained popularity which constantly monitor a patient’s signals. These gadgets acquire the data and transmit it to the cloud for further processing. Power consumption due to data transmission is a major issue in these devices. Moreover, in a constant monitoring environment, the number of classes to be identified are usually higher and overlapping. Existing techniques either require the entire data to be transmitted, such as in deep learning, or suffer from reduced accuracy. In this context, we propose a new framework for EEG based epilepsy detection which requires a low data transmission while maintaining high accuracy for multiclass classification. At the device-end, we use a preprocessing mechanism that uses adaptive rate sampling, modified activity selection, filtering, and wavelet decomposition to extract only a handful of highly discriminatory features to be transmitted instead of the entire EEG waveform. For multiclass classification, we propose a novel ensemble of sub-problems-based classification paradigm to achieve high accuracy using the reduced data. Our proposed solution shows many-fold increase in computational gains and an accuracy of 100% and 99.38% on the 2-class problem when tested on the popular University of Bonn and CHB-MIT datasets, respectively. An accuracy of 99.6% on 3-class, 96% on 4-class, and 92% on 5-class problems is obtained for the University of Bonn dataset.
dc.language.isoen_US
dc.subjectFeature selectionen_US
dc.subjectEnsemble classifieren_US
dc.titleEpileptic seizure classification using level-crossing EEG sampling and ensemble of sub-problems classifieren_US
dc.typeBook chapter
dc.source.booktitleExpert Systems with Applications
dc.source.volume191
dc.contributor.researcherElectrical and Computer Engineering


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