EEG Classification during Scene Free-Viewing for Schizophrenia Detection
Author
dc.contributor.author
Devia, Christ
Author
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Mayol Troncoso, Rocío
Author
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Parrini, Javiera
Author
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Orellana, Gricel
Author
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Ruiz, Aida
Author
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Maldonado Arbogast, Pedro
Author
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Egana, Jose Ignacio
Admission date
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2019-10-30T15:22:30Z
Available date
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2019-10-30T15:22:30Z
Publication date
dc.date.issued
2019
Cita de ítem
dc.identifier.citation
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Volumen 27, Issue 6, 2019, Pages 1193-1199
Identifier
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15580210
Identifier
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15344320
Identifier
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10.1109/TNSRE.2019.2913799
Identifier
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https://repositorio.uchile.cl/handle/2250/172263
Abstract
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Currently, the diagnosis of schizophrenia is made solely based on interviews and behavioral observations by a trained psychiatrist. Technologies such as electroencephalography (EEG) are used for differential diagnosis and not to support the psychiatrist's positive diagnosis. Here, we show the potential of EEG recordings as biomarkers of the schizophrenia syndrome. We recorded EEG while schizophrenia patients freely viewed natural scenes, and we analyzed the average EEG activity locked to the image onset. We found significant differences between patients and healthy controls in occipital areas approximately 500 ms after image onset. These differences were used to train a classifier to discriminate the schizophrenia patients from the controls. The best classifier had 81% sensitivity for the detection of patients and specificity of 59% for the detection of controls, with an overall accuracy of 71%. These results indicate that EEG signals from a free-viewing paradigm discriminate patients from healthy controls and have the potential to become a tool for the psychiatrist to support the positive diagnosis of schizophrenia.
Lenguage
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en
Publisher
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Institute of Electrical and Electronics Engineers Inc.