# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 1 Introduction  
In the medical field, diseases are often diagnosed by means of laboratory tests, biological markers, or by imaging modalities. However, the diagnosis of diseases encompassing psychiatric disarray is predominantly based on interviews from patients, symptoms presented, and the existence or absence of representative behavioral signs [1]. Schizophrenia (SZ) is a severe, prolonged disorder of the brain that interrupts normal thinking, speech, and the behavioral characteristics of an individual [2]. The National Institute of Mental Health views SZ as a significant contributor to disease burden, with about 2.4 million people in the United States over the age of 18 effected by it [3]. Moreover, the World Health Organization reports that more than 21 million people are affected by SZ worldwide. Schizophrenia is a manifestation of a constellation of symptoms that can include hallucinations, hearing voices that are non-existent, disorganized speech, and functional deterioration, among many others. Environmental perils such as premature birth, low birth weight, perinatal hypoxia, exposure to an intrauterine virus at an early stage of life, and stressors related to social isolation, migrant status, and urban life at adulthood, subtly hamper brain development, which can lead to the disease [4]. Additionally, since SZ is highly genetic [5], individuals with the genes Neurogranin and Zinc Finger Protein 804A have an increased risk of developing it [6]. Quality of life is then compromised, with most SZ patients being unable to function in workplaces, 20-40% attempting suicide at least once, and between 5-10% successfully committing suicide [7]. Hence, precise and timely prognosis is desired for better treatment and for the recovery of the patient. To date, there is not an established clinical test for SZ, and diagnosis relies on behavioral markers observed by experts. Such assessments are subjective and not very accurate; they fail to capture underlying abnormalities taking place within the brain. Neuroimaging techniques using multimodal imaging are currently used to detect SZ. Some of these modalities include magnetic resonance imaging, positron emission tomography, functional magnetic resonance imaging, and diffusion tensor magnetic resonance imaging. A combination of the above-mentioned methods may be useful when one imaging modality alone does not explicate the neurological disease of the patient [8]. However, employing a combination of imaging devices may not only be costly for implementation, but also the fusion of images acquired from two different devices may not be of sufficient quality due to motion artifacts [9]. Hence, a more cost-effective method of diagnosing SZ is needed. Electroencephalograms (EEG) are signals which characterize the electrical activity of the human brain recorded from the scalp. It would be very helpful to automatically detect neurological disorders such as epilepsy, depression [10] Parkinson's disease [11] and Alzheimer's disease [12; 13] by computerized means. Recent studies have analyzed EEG signals for SZ diagnosis [14; 15]. Table 1 summarizes the published studies of computer-aided detection (CAD) systems using EEG for SZ classification. [PERSON] et al. [16] extracted EEG recordings with 21 gold cup electrodes placed according to the 10-20 international system, as the horizontal and vertical eye movements of participants were monitored. MATLAB and EGGLAB toolboxes [17] were employed to pre-process the signals, and five frequency bands were selected for analysis. The spectral power of EEG data was computed with fast Fourier transformation, after which the EEG power deviations were studied using the analysis of variance (ANOVA) measure for each of the five frequency bands examined. The diagnostic performance of a test used to distinguish between normal and SZ patients was evaluated with receiver operating curve (ROC) analysis. The delta power was reported to have the highest classification accuracy, at 62.2%. [PERSON] et al.