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Schizophrenia (SZ) is a mental disorder whereby due to the secretion ofspecific chemicals in the brain, the function of some brain regions is out ofbalance, leading to the lack of coordination between thoughts, actions, andemotions. This study provides various intelligent deep learning (DL)-basedmethods for automated SZ diagnosis via electroencephalography (EEG) signals.The obtained results are compared with those of conventional intelligentmethods. To implement the proposed methods, the dataset of the Institute ofPsychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signalswere divided into 25 s time frames and then were normalized by z-score or normL2. In the classification step, two different approaches were considered for SZdiagnosis via EEG signals. In this step, the classification of EEG signals wasfirst carried out by conventional machine learning methods, e.g., supportvector machine, k-nearest neighbors, decision tree, na\"ive Bayes, randomforest, extremely randomized trees, and bagging. Various proposed DL models,namely, long short-term memories (LSTMs), one-dimensional convolutionalnetworks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step,the DL models were implemented and compared with different activationfunctions. Among the proposed DL models, the CNN-LSTM architecture has had thebest performance. In this architecture, the ReLU activation function with thez-score and L2-combined normalization was used. The proposed CNN-LSTM model hasachieved an accuracy percentage of 99.25%, better than the results of mostformer studies in this field. It is worth mentioning that to perform allsimulations, the k-fold cross-validation method with k = 5 has been used.

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models