Publishing frequency for psychiatric EEG studies using Deep Learning (DL) has been increasing.
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This study systematically reviews articles according to three domains: clinical features, EEG-processing and Deep Learning.
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Many of the reviewed studies lack a clear and meaningful description of the clinical population.
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Many of these reviewed studies select erroneous testing procedures which compromise the quality and impact of their findings.
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Several basic but critical recommendations for conducting future research in this field are offered.
Abstract
In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.
