This study presents a method that aims to automatically diagnose Schizophrenia (SZ) patients by using EEG recordings. Unlike many literature studies, the proposed method does not manually extract features from EEG recordings, instead it transforms the raw EEG into 2D by using Short-time Fourier Transform (STFT) in order to have a useful representation of frequency-time features. This work is the first in the relevant literature in using 2D timefrequency features for the purpose of automatic diagnosis of SZ patients. In order to extract most useful features out of all present in the 2D space and classify samples with high accuracy, a state-of-art Convolutional Neural Network architecture, namely VGG-16, is trained. The experimental results show that the method presented in the paper is successful in the task of classifying SZ patients and healthy controls with a classification accuracy of 95% and 97% in two datasets of different age groups. With this performance, the proposed method outperforms most of the literature methods. The experiments of the study also reveal that there is a relationship between frequency components of an EEG recording and the SZ disease. Moreover, Grad-CAM images presented in the paper clearly show that mid-level frequency components matter more while discriminating a SZ patient from a healthy control.
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A Tunable Q-Factor Wavelet Transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control (HC) subjects was performed using the Kruskal Wallis test. Feature values obtained from each sub band were classi ed using well-known ensemble learning techniques and their classi cation performances were tested. Among the evaluated classi ers, the highest classi cation performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
Alzheimer beyindeki bozulmalardan kaynaklı bilişsel ve davranışsal eksiklikler gibi semptomlarla kendini gösteren önemli bir nörolojik hastalıktır. Alzheimer hastalığının kesin bir tedavi yöntemi bulunmamaktadır. Ancak hastalığın erken teşhisi ile hastalığın ilerlemesinin yavaşlatılması amaçlanmaktadır. Bu durum hastanın yaşam standartlarının korunmasında önem arz etmektedir. Ayrıca hastalığın tam olarak teşhisi deneyimli bir uzman tarafından değerlendirilecek olan maliyetli testler ve yorucu bir teşhis aşaması gerektirmektedir. Bu motivasyonla önerilen yöntemle Alzheimer hastalığının EEG sinyallerinden otomatik olarak gerçekleştirilmesini amaçlayan yeni bir bilgisayar destekli tanı sistemi sunulmaktadır. Sunulan çalışmada öncelikle ham EEG verilerine önişlem uygulanarak var olan gürültüler giderilmiştir. Sonraki aşamada ise her bir kanaldan alınan verilere dalgacık dönüşümü uygulandıktan sonra istatistiksel özellikler hesaplanmıştır. Elde edilen özelliklerin k-en yakın komşu (kNN) sınıflandırıcısı ile sınıflandırılmasıyla sağlıklı katılımcılar ile Alzheimer hastası katılımcılar 91.12% doğrulukla ayırt edilmiştir.
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