Analysis of the Brainwaves for the Diagnosis of Schizophrenia with Deep Learning Methods

Year 2023, Volume: 5 Issue: 2, 325 - 337, 27.10.2023

Berkay Serin , Sevcan Emek

https://doi.org/10.46387/bjesr.1332678

Abstract

Techniques based on the mathematical model of the human brain provide a tracking system for thinking, memory, perception, speech, and other vital activities. This study offers an alternative approach to diagnosing schizophrenia. In this study, data obtained from 14 schizophrenic patients and 14 healthy individuals, using standard 10-20 EEG montages with 19 EEG channels are used. These data are classified in different ways and the findings obtained from the experimental studies are compared in terms of accuracy and time spent. Although the first approach is to conduct the brainwaves with Convolutional Neural Networks (CNN) without processing, it is observed that it does not yield an efficient result, because CNN forgets the wave data associated with each other between each neuron layer. For this reason, Recurrent Neural Network (RNN) is used to maintain the integrity of the data. Throughout the study, brainwaves are classified with suggested deep learning methods and it is tried to reach the most efficient one.

Keywords

Brainwaves, Classification, Deep Learning, CNN, RNN

Şizofreni Hastalığının Tanısına Yönelik Beyin Dalgalarının Derin Öğrenme Yöntemleri ile İncelenmesi

Abstract

İnsan beyninin matematiksel modeli üzerine kurulan tekniklerin kullanılması, insan için hayati olan düşünme, hafıza, algılama, konuşma ve diğer yaşam aktivitelerinin sürdürülmesinde bir takip sistemi oluşturmaktadır. Bu çalışmada şizofreni hastalığın teşhis edilmesine yönelik alternatif bir yaklaşım sunulmaktadır. Çalışmada, 14 şizofreni hastası ve 14 sağlıklı bireyden alınmış, 19 EEG kanalıyla standart 10-20 EEG montajı kullanılarak 250 Hz örnekleme frekansı ile elde edilen veriler kullanılmaktadır. Çalışma boyunca, bu veriler farklı şekillerde sınıflandırılmakta ve deneysel çalışmalarla elde edilen bulgular doğruluk ve harcanan süre açısından karşılaştırılmaktadır. İlk yaklaşım olarak, beyin dalgalarının işlenmeden evrişimli sinir ağları (Convolutional Neural Network -CNN) ile yürütmek olsa da CNN her nöron katmanı arasında birbiriyle ilişkili dalga verilerini unuttuğu için verimli bir sonuç vermediği gözlemlenmektedir. Bu nedenle, verilerin bütünlüğünü koruyacak tekrarlayan sinir ağları (Recurrent Neural Network -RNN) kullanılmaktadır. Çalışma genelinde, beyin dalgaları önerilen ve yapılandırılmış derin öğrenme yöntemleri ile sınıflandırılmakta ve en verimli olanına ulaşmaya çalışılmaktadır.

Keywords

Beyin Dalgası, Sınıflama, Derin Öğrenme, CNN, RNN

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