Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models - Shoeibi, Afshin; Sadeghi, Delaram; Moridian, Parisa; Ghassemi, Navid; Heras, Jónathan; Alizadehsani, Roohallah; Khadem, Ali; Kong, Yinan; Nahavandi, Saeid; Zhang, Yu-Dong; Gorriz, Juan Manuel (Frontiers in Neuroinformatics, 2021)
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent Deep Learning (DL)-based methods for automated SZ diagnosis via EEG signals. The obtained results are compared with those of conventional intelligent methods. In order to implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals are divided into 25-seconds time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches are considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals is first carried out by conventional DL methods, e.g., KNN, DT, SVM, Bayes, bagging, RF, and ET. Various proposed DL models, including LSTMs, 1D-CNNs, and 1D-CNN-LSTMs, are used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function and the z-score and L2 combined normalization are used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that in order to perform all simulations, the k-fold cross-validation method with k=5 has been used.