Key result
The Frequency Channel-based convolutional neural network (FCCNN) identified Major Depressive Disorder from EEG signals with an accuracy of 99%, sensitivity of 99.07%, and specificity of 98.90%.
Why the study?
Online EEG classification to assess and track MDD status remains challenging due to intensive noises, intrinsic non-stationarity, and lack of effective decoupling between neural networks and brain states.
Does a Frequency Channel-based CNN improve the accuracy of identifying Major Depression Disabled from EEG data compared to other classifiers?
Does a Frequency Channel-based CNN improve the accuracy of identifying Major Depression Disabled from EEG data compared to other classifiers?
A Frequency Channel-based CNN with an attention mechanism achieved high accuracy (99%) in identifying major depression from EEG signals.
Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group.
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Ke et al. (2021) studied Major Depressive Disorder (MDD) (n=64). Frequency Channel-based convolutional neural network (FCCNN) vs. Other machine learning models (e.g., LeNet, Resnet-16, GoogLeNet) was evaluated on Classification accuracy for identifying MDD. The Frequency Channel-based convolutional neural network (FCCNN) identified Major Depressive Disorder from EEG signals with an accuracy of 99%, sensitivity of 99.07%, and specificity of 98.90%.
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