A deep learning model (MRCNN-RSE) using frontal six-channel 8-30 Hz EEG signals achieved a classification accuracy of 98.48 ± 0.22% for diagnosing depressive disorder.
Observational (n=75)
Can deep learning models applied to frontal six-channel EEG signals accurately diagnose Depressive Disorder?
A deep learning framework using frontal six-channel EEG signals achieved high accuracy in diagnosing depressive disorder, offering a potential objective diagnostic tool.
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
Xu et al. (Mon,) conducted a observational in Depressive disorder (n=75). Frontal six-channel EEG signals and deep learning models (MRCNN-RSE) vs. Healthy controls was evaluated on Classification accuracy for DD diagnosis. A deep learning model (MRCNN-RSE) using frontal six-channel 8-30 Hz EEG signals achieved a classification accuracy of 98.48 ± 0.22% for diagnosing depressive disorder.
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