Interpretation of Frequency Channel-Based CNN on Depression Identification

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.

[1]  Yi-Hung Liu,et al.  Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns , 2017, Sensors.

[2]  C. McClung,et al.  Rhythms of life: circadian disruption and brain disorders across the lifespan , 2018, Nature Reviews Neuroscience.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Benyun Shi,et al.  Improving Brain E-Health Services via High-Performance EEG Classification With Grouping Bayesian Optimization , 2020, IEEE Transactions on Services Computing.

[6]  Aamir Saeed Malik,et al.  A wavelet-based technique to predict treatment outcome for Major Depressive Disorder , 2017, PloS one.

[7]  Zuowei Shen,et al.  Deep Learning via Dynamical Systems: An Approximation Perspective , 2019, Journal of the European Mathematical Society.

[8]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[9]  Rajiv Ranjan,et al.  Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC , 2018, IEEE Access.

[10]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Han Wang,et al.  Recognizing Brain States Using Deep Sparse Recurrent Neural Network , 2019, IEEE Transactions on Medical Imaging.

[13]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[14]  Xiaoli Li,et al.  Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN , 2020, Softw. Pract. Exp..

[15]  M. Putten,et al.  Detecting abnormal electroencephalograms using deep convolutional networks , 2019, Clinical Neurophysiology.

[16]  Thomas Wiatowski,et al.  A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.

[17]  Na Lu,et al.  Motor imagery classification via combinatory decomposition of ERP and ERSP using sparse nonnegative matrix factorization , 2015, Journal of Neuroscience Methods.

[18]  Tonio Ball,et al.  Machine-learning-based diagnostics of EEG pathology , 2020, NeuroImage.

[19]  Brendon O. Watson,et al.  Gamma oscillations as a biomarker for major depression: an emerging topic , 2018, Translational Psychiatry.

[20]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Gahangir Hossain,et al.  Seizure Prediction and Detection via Phase and Amplitude Lock Values , 2016, Front. Hum. Neurosci..

[22]  Zizhuo Wang,et al.  Probabilistic Forecasting with Temporal Convolutional Neural Network , 2019, Neurocomputing.

[23]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[24]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).