A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data

• Published in: Computers in biology and medicine Vol. 151; no. Pt A; p. 106225
• Main Authors: Supakar, Rinku, Satvaya, Parthasarathi, Chakrabarti, Prasun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2022; Elsevier Limited
• Subjects: Accuracy; Algorithms; Anomalies; Automation; Brain; Classification; Classifiers; CNN; Datasets; Deep Learning; Discriminant analysis; Disease; EEG; Electroencephalogram; Electroencephalography; Electroencephalography - methods; Feature extraction; Hallucinations; Humans; Internal Medicine; Machine Learning; Magnetic resonance imaging; Mental disorders; Model accuracy; Neural networks; Other; Performance evaluation; Psychosis; RNN-LSTM; Schizophrenia; Schizophrenia - diagnosis; Deep learning; Schizophrenia; CNN; Electroencephalogram; RNN-LSTM
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106225

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep learning is capable of automatically extracting the significant features and make classifications, the authors proposed a deep learning based model using RNN-LSTM to analyze the EEG signal data to diagnose schizophrenia. The proposed model used three dense layers on top of a 100 dimensional LSTM. EEG signal data of 45 schizophrenic patients and 39 healthy subjects were used in the study. Dimensionality reduction algorithm was used to obtain an optimal feature set and the classifier was run with both sets of data. An accuracy of 98% and 93.67% were obtained with the complete feature set and the reduced feature set respectively. The robustness of the model was evaluated using model performance measure and combined performance measure. Outcomes were compared with the outcome obtained with traditional machine learning classifiers such as Random Forest, SVM, FURIA, and AdaBoost, and the proposed model was found to perform better with the complete dataset. When compared with the result of the researchers who worked with the same set of data using either CNN or RNN, the proposed model's accuracy was either better or comparable to theirs.