Detection and Classification of Abnormality in Electroencephalogram Signals Using Deep Learning and Convolutional Neural Networks

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

The human body is combined of a group of subsystems that communicate with each other, wherethe brain is the control unit for these systems. The brain function through a group of small partscalled neurons, groups of neurons is connected to create a neural network to perform some tasks.When these neurons have issues or give wrong signals the brain will start to confuse the bodysystems and that can lead to neurological problems such as Epilepsy. To understand and diagnosesuch issues a test called Electroencephalogram (EEG) is performed. It records the electricalactivities of the brain so that each part of this record can refer to an activity or group of activities.The issue with EEG is that it produces a huge amount of data for a short recording time, and dueto the complexity of the brain signals and human errors, doctors are taking lots of time to diagnosethe records, and many patients are misdiagnosed. This leads to the need to have a computerizedsystem that can reduce these problems.Many systems were proposed in the previous years, and with the advantages of AI and MachineLearning, much research was done in this field. Some applications were created using rule-basedsystems, others using Multilayer Perceptron (MLPs). When Deep Learning Networks such asConvolutional Neural Networks (CNNs) starts to be popular, many applications also were طbسنlt‎based on them it. One main challenge of EEG signal processing is that the patterns are notnecessarily unique; where the same signal for different patients can mean different things, whichmakes it very hard to create a generic model for EEG signal processing.In this research, a new approach is proposed where we take advantage of the CNN abilities toextract features and handle complex time-series signals, combine it with wavelet signaldecomposition along with preprocessing steps to create a robust model to analyze the EEG dataand detect the epileptic seizures. The main strength of this work is that it’s built at the patient level;since the EEG test provides a huge amount of data it’s possible to tune the model for each patient.In this work, a 1D CNN model with ConvlD, Long Short-Term Memory (LSTM), and MLPSlayers was created. A global dataset for several patients who are suffering from epilepsy was usعd.‎In this research, a comparison between our work and other regular algorithms such as RegressionTree, K-Nearest Neighbors (KNN(, Support Vector Machines )SVM), and Ensemble was done.The algorithms we selected were based on the data structure and the studies that were done in thisfield. The proposed preprocessing algorithm enhances the data and made it easier to analyze,whereas the proposed detection model provided much better detecting accuracy when it’s usedwith the processed data. Also, the regular algorithms did so based on the previous studies that werereviewed. The final average epileptic signals detection accuracy is 97.14%, with the highestaccuracy of 99.2%. Originally this research was targeting the local Palestinian data, unfortunately,such kind of data was not available and due to that, a global data set was used.

روابط وملفات

التعريف والنوع

رقم الوثيقة

032043f1-e724-49a0-8298-2862c3be442e

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2441911_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Khaleel Kareem Tayseer Sadeq","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"محمد عواض","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Detection and Classification of Abnormality in Electroencephalogram Signals Using Deep Learning and Convolutional Neural Networks

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

delta waves,gamma waves,algorithms,networks,beta waves

إشراف وإعداد

الإشراف

محمد عواض

الإعداد

Khaleel Kareem Tayseer Sadeq

الاقتباسات الببليوغرافية