Klasifikasi Data Penderita Skizofrenia Menggunakan CNN-LSTM dan Cnn-Gru pada Data Sinyal EEG 2D - Firmansyah; Rini, Dian Palupi; Sukemi (Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET), 2023)
Schizophrenia (SZ) is a brain disease with a chronic condition that affects the ability to think. Common symptoms that are often seen in SZ patients are hallucinations, delusions, abnormal behavior, speech disorders, and mood disorders. SZ patients can be diagnosed using electroencephalographic (EEG) signals. This study conducted a comparative analysis of the best method in EEG classification using the Deep Learning (DL) method. The author uses the 2D Convolutional Neural Network (2D-CNN) method with different layers. The first 2D-CNN uses a layer of Long Short Term memory(LSTM) and Gate Recurrent Unit(GRU). The dataset used consists of two types of EEG signals obtained from 39 healthy individuals and 45 schizophrenic patients during a resting state. Test results for the accuracy of the F1-score from 5 times testing the CNN method using the LSTM layer has the best accuracy value of 94.12% and 5 times testing the CNN method using the GRU layer has the best accuracy value of 94.12%.