Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique

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dc.contributor.authorSaminu, Sani
dc.contributor.authorGuizhi, Xu
dc.contributor.authorZhang, Shuai
dc.contributor.authorIsselmou, Abd El Kader
dc.contributor.authorZakariyya, Rabiu Sale
dc.contributor.authorJabire, Adamu Halilu
dc.date.accessioned2022-01-10T10:33:54Z
dc.date.available2022-01-10T10:33:54Z
dc.date.issued2019-12-10
dc.description.abstractEpilepsy is a type of neurological disorder which can happen without serious warning and affects people almost at any age. It is a brain disorder caused by sudden and unprovoked seizures as a result of excitation of a lot of brain cells simultaneously which may lead to physical symptoms abnormalities and deformation such as failure in concentration, memory, attention etc. therefore, proper and efficient method of continues monitoring and detection of these epileptic seizures is paramount. This work presents an effective and efficient technique suitable for smart, low cost, power and real time devices that can be easily integrated with recent 5G network IoT devices for mobile applications, home and health care centers for monitoring and alert the doctors and patients about its occurrence to prevent a sudden collapse and consciousness which may cause injury and death. We proposed a low computational cost features extraction method by utilizing the efficacy of time-frequency, statistical and non-linear features known as hybrid techniques. The efficiency and accuracy of these smart devices is highly depends on quality of feature extraction methods and classifier performance. Therefore, this work employed two machine learning classifiers, support vector machine (SVM) and feedforward neural network (FFNN) to detect and classify interictal (normal) and ictal (seizure) signals. Discrete wavelet transform (DWT) is employed to decomposes the signals into decomposition levels as sub-bands of the signals to capture the non-stationarity of the EEG signals. Mean, median, maximum, minimum etc. were calculated for each sub-band as statistical parameters, non-linear features such as sample entropy, approximate entropy and wavelet energy were also calculated. The combination of features is then fed to two classifiers for the classification. Based on the performance measures such as accuracy, sensitivity and specificity, our proposed approach reveals a promising result with highest accuracy of 99.6%.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7280
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDWTen_US
dc.subjectFFNNen_US
dc.subjectSVMen_US
dc.subjectEEGen_US
dc.subject5G Networken_US
dc.subjectEpilepsy Detectionen_US
dc.subjectSeizure Detectionen_US
dc.subjectInternet of Thingsen_US
dc.titleEpilepsy Detection and Classification for Smart IoT Devices Using hybrid Techniqueen_US
dc.typeArticleen_US
dc.typePresentationen_US

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