Analysis of significant EEG frequency Bands for Epilepsy Disease using the Self-Organizing map
Date
2022-07-20
Authors
Mukhammad Ziyad Khalel Abdelkader
محمد زياد خليل عبد القادر
Journal Title
Journal ISSN
Volume Title
Publisher
Al-Quds University
Abstract
تهدف هذه الرسالة إلى إيجاد أثر مرض الصرع على نطاقات التردد الخمسة لموجات الدماغ عند الانسان. و لقد شملت هذه الرسالة على عينة من 28 شخصا مصابون بمرض الصرع , حيث تم تسجيل و رصد إشارات الدماغ لهم عبر جهاز تخطيط إشارات الدماغ و حفظها في قاعدة بيانات باستخدام جهاز الحاسوب.
تم إجراء تحليل لإشارات الدماغ, باستخدام تحويلات فورييه السريعة و ذلك من أجل الحصول على طاقة النطاقات الخمسة ثم إدخال هذه البيانات كمدخلات في خريطة التنظيم الذاتي, و قد تبين من هذه الدراسة وجود تباين واضح بين بيانات المرضى و السليمين , حيث ظهر أنه يوجد ارتفاع كبير في طاقة موجة ثيتا للمصابين بمرض الصرع.
توضح هذه الدراسة أهمية استخدام الذكاء الاصطناعي و تحديد الشبكات العصبية الاصطناعية وهنا نركز على تقنية خرائط التنظيم الذاتي, لأن لها القدرة على عرض معلومات كبيرة و ذات أبعاد عالية, على شكل شبكة في بعدين يسهل فهمها , وهذا ما قمنا به على مرض الصرع.
This thesis aims to find the effect of epilepsy on the five frequency bands of human brain waves. And this thesis included a sample of 28 people with epilepsy, where their brain signals were recorded and monitored through an EEG device and saved in a database using a computer. An analysis of brain signals was carried out, using fast Fourier transforms, in order to obtain the energy of the five bands and then enter these data as inputs in the self-organizing map, and it was found from this study that there is a clear discrepancy between the data of patients and healthy ones, where it appeared that there is a large increase in the energy of theta wave for people with epilepsy. This study demonstrates the importance of using artificial intelligence and identifying artificial neural networks, and here we focus on the technique of self-organizing maps, because it has the ability to provide large and high-dimensional information in the form of a network in two dimensions that is easy to understand, and this is what we have done on epilepsy.
This thesis aims to find the effect of epilepsy on the five frequency bands of human brain waves. And this thesis included a sample of 28 people with epilepsy, where their brain signals were recorded and monitored through an EEG device and saved in a database using a computer. An analysis of brain signals was carried out, using fast Fourier transforms, in order to obtain the energy of the five bands and then enter these data as inputs in the self-organizing map, and it was found from this study that there is a clear discrepancy between the data of patients and healthy ones, where it appeared that there is a large increase in the energy of theta wave for people with epilepsy. This study demonstrates the importance of using artificial intelligence and identifying artificial neural networks, and here we focus on the technique of self-organizing maps, because it has the ability to provide large and high-dimensional information in the form of a network in two dimensions that is easy to understand, and this is what we have done on epilepsy.