Abstract
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.
Highlights
More than 350 million people are suffering from depression in the world according to the report of the WHO
To this end, inspired by attention mechanisms (Vaswani et al, 2017) and time-frequency analysis, we propose a Frequency Channel-based convolutional neural network (CNN) (FCCNN) to identify depression accurately and quickly
We conducted the experiments to evaluate the performance of the proposed approach upon one public available EEG data set of Major Depression Disabled (MDD), which consisted of (1) a performance study for MDD identification; (2) an experiment on the interpretation of classifier; and (3) an experiment on the analysis of attention block
Summary
More than 350 million people are suffering from depression in the world according to the report of the WHO. Mumtaz et al (2017) proposed a machine learning method to classify features extracted by wavelet transform of EEG signals and achieve high-performance. It is necessary to decouple the black box by measuring the complex relationship between the key features of the brain regarding channels (brain regions) and the model To this end, inspired by attention mechanisms (Vaswani et al, 2017) and time-frequency analysis, we propose a Frequency Channel-based CNN (FCCNN) to identify depression accurately and quickly. It combines the brain rhythm with the attention mechanism of the classifier aiming at focusing on the features of interest. The performance of this solution is overwhelmingly higher than the state-of-the-art methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
