Epilepsy Detection and Classification for Smart IoT Devices Using hybrid Technique
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Date
2019-12-10
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Epilepsy 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%.
Description
Keywords
DWT, FFNN, SVM, EEG, 5G Network, Epilepsy Detection, Seizure Detection, Internet of Things