Research On Depression's Real-time Monitoring Based On Electroencephalograph And Convolutional Neutral Network

In recent years,more and more studies have begun to focus on the quantitative analysis of physiological signals represented by electroencephalogram(EEG)in order to achieve a more objective and effective diagnosis of mental disorders such as depression.However,depression is a kind of mental disorder with very complex and dynamic changes over time.Its effective diagnosis often depends on the continuous evaluation of the patient's psychological status.However,the traditional multi-channel EEG acquisition device is difficult to achieve effective tracking of the patient's EEG data due to its high price and need of special operations,and it is urgent to provide a solution for long-term monitoring and tracking acquisition of the patient EEG.At the same time,because the EEG signal has characteristics such as non-linearity,non-stationarity,and individual physiological differences,how to extract the features of the EEG signals that can better reflect the patient's psychological state and build a prediction model with stronger generalization ability become an important prerequisite for effective diagnosis and monitoring of depression.In order to solve the above problems,this paper builds a framework for tracking acquisition and real-time quantitative evaluation of pervasive EEG signals based on wearable EEG sensors,and uses the Convolutional Neural Network(CNN)to build a classification model of depression for the pervasive EEG,which draws on the advantages of automatic feature extraction,excellent spatial characteristics,and new classification pattern discovery capabilities in large amounts of data to ensure rapid model update and generalization capabilities.The main work and contributions include the following three aspects:1)Combined with the EEG real-time acquisition module and the depression risk prediction module,a two-stage real-time depression monitoring method is designed: the real-time EEG acquisition stage mainly realizes real-time filtering,real-time eye artifact elimination,and data visualization,which make the traditional EEG acquisition rise to long-term monitoring of depression;the depressive risk prediction stage mainly implements multiple classification model decision fusion to give the participants' depression risk assessment.2)The exploration of the CNN network structure in the resting state EEG starts with the shallow network and gradually analyzes the influence of different selection of network parameters on the final classification effect.For the problems faced in shallow CNN,a more robust classification model of depression is proposed: neural network based on feature maps and deep residual network based on residual connections.Compared with traditional classification algorithms,these two methods based on CNN can fully utilize the geographic association between the electrodes,which makes the classification performance significantly improved.The feature map neural network achieves an average accuracy of 76.33%,and the deep residual network reaches an average accuracy of 80.33%.3)For the EEG that lead into the audio experiment paradigm,a classification model combining CNN and Long Short-term Memory(LSTM)is constructed.Different from the original signal used in the resting state EEG,the time-stamped EEG data needs to be converted into a more easilyanalyzed picture mode and turned into a task similar to video classification.The convolution operation can use the spatial information to automatically extract features in advance,while the LSTM models the convolution output results in time series.This article compares three main temporal and spatial information extraction methods and includes three feature extractors: time domain convolution,CNN + LSTM,and ConvLSTM.Among them,ConvLSTM achieved the best 82.35% classification accuracy.