2022
DOI: 10.3389/fpsyt.2021.813460
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From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

Abstract: Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP compone… Show more

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Cited by 24 publications
(6 citation statements)
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“…In the field of audio processing today, DL models, particularly the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved remarkable success [37][38][39]. CNN is mainly used to process data with the grid structure, and it can extract local features in input data through convolution operation [40].…”
Section: A the DL Model For Extracting Tune Characteristics Of Operamentioning
confidence: 99%
“…In the field of audio processing today, DL models, particularly the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved remarkable success [37][38][39]. CNN is mainly used to process data with the grid structure, and it can extract local features in input data through convolution operation [40].…”
Section: A the DL Model For Extracting Tune Characteristics Of Operamentioning
confidence: 99%
“…For the same reasons as in obsessive-compulsive disorder, EEG and MRI remain the predominant neuroimaging modalities in the context of artificial intelligence research related to schizophrenia. In a study conducted in 2022, researchers utilized an openly accessible Kaggle dataset to investigate alterations in electroencephalographic patterns during auditory processing and their potential in discriminating between individuals with schizophrenia and healthy controls (Roach, 2021); (Barros et al, 2022). In a study conducted in 2020, head magnetic resonance (MR) images of individuals diagnosed with schizophrenia were acquired from multiple centers.…”
Section: Schizophreniamentioning
confidence: 99%
“…For instance, Baraditis demonstrated that microstate alterations obtained by resting state EEG and analyzed with SVM supervision were able to discriminate individuals with schizophrenia by healthy controls, with high accuracy (82.7%), sensitivity (83.5%), and specificity (85.3%) [44]. Deep learning convolution neural network was successfully applied to examine multi channels auditory related EEG single trials to distinguish subjects with schizophrenia from healthy controls with an accuracy of 78% [45]. A linear discriminant analysis classifier was successfully applied to test if resting state EEG was not only able to discriminate schizophrenia patients from controls (with an accuracy of 80.66%) but also to stratify patients based on their symptom severity, with an accuracy as high as 88.10% for positive symptoms [46].…”
Section: Socio-demographic Factorsmentioning
confidence: 99%