EEG-Based Schizophrenia Diagnosis through Time Series Image Conversion and Deep Learning

Abstract

Schizophrenia, a mental disorder experienced by more than 20 million people worldwide, is emerging as a serious issue in society. Currently, the diagnosis of schizophrenia is based only on mental disorder diagnosis and/or diagnosis by a psychiatrist or mental health professional using DSM-5, a diagnostic and statistical manual of mental disorders. Furthermore, patients in countries with insufficient access to healthcare are difficult to diagnose for schizophrenia and early diagnosis is even more problematic. While various studies are being conducted to solve the challenges of schizophrenia diagnosis, methodology is considered to be limited, and diagnostic accuracy needs to be improved. In this study, a new approach using EEG data and deep learning is proposed to increase objectivity and efficiency of schizophrenia diagnosis. Existing deep learning studies use EEG data to classify schizophrenic patients and healthy subjects by learning EEG in the form of graphs or tables. However, in this study, EEG, a time series data, was converted into an image to improve classification accuracy, and is then studied in deep learning models. This study used EEG data of 81 people, in which the difference in N100 EEG between schizophrenic patients and healthy patients had been analyzed in prior research. EEGs were converted into images using time series image conversion algorithms, Recurrence Plot (RP) and Gramian Angular Field (GAF), and converted EEG images were learned with Convolutional Neural Network (CNN) models built based on VGGNet. When the trained deep learning model was applied to the same data from prior research, it was demonstrated that classification accuracy improved when compared to previous studies. Among the two algorithms used for image conversion, the deep learning model that learned through GAF showed significantly higher classification accuracy. The results of this study suggest that the use of GAF and CNN models based on EEG results can be an effective way to increase objectivity and efficiency in diagnosing various mental disorders, including schizophrenia.

Exploring deep residual network based features for automatic schizophrenia detection from EEG

“…In recent years, many research studies have been performed to acquire informative features from EEG signals for the detection of schizophrenia [15,16,17,18,19,20,21,22,23,24,25,26,27,28]. These studies reported various algorithms based on various features to characterize the.…”

“…In literature, very few research works have been performed on deep learning (DL) for detection of schizophrenia in EEG (e.g. we found few works [ 24 , 26 , 28 ]) but the methods are still limited in their ability to balance the efficiency and accuracy of schizophrenia detection. In addition, we also explored some recent works of schizophrenia detection that used Magnetic resonance images (MRI) data [ 29 , 30 , 31 , 32 , 33 , 34 ] as reported in Table 1 .…”

Regional Contribution in Electrophysiological-Based Classifications of Attention Deficit Hyperactive Disorder (ADHD) Using Machine Learning

“…EEG signals have been utilized in the diagnosis of various neurological disorders by extracting distinctive features and employing different classifiers in automated detection systems. EEG signals have been employed for the automatic detection of neurophysiological conditions such as alcoholism [7], dementia [8], epilepsy [9], schizophrenia [10], Parkinson's disease [11], and depressive disorders [12]. The EEG patterns of children with ADHD display differences in complexity, randomness, amplitude, and frequency compared with those of typically developing children.…”

Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach

“…Moreover, research plays a vital role in challenging misconceptions, reducing stigma, and promoting a more inclusive and supportive environment for individuals living with schizophrenia. Electroencephalography (EEG), while not a standalone diagnostic tool for schizophrenia, offers valuable insights into the disorder's neural dynamics and functional abnormalities due to its high temporal resolution [6][7][8].…”