An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

To address the problem of low accuracy in multi-classification recognition of motor imagery electroencephalogram(EEG)signals,a recognition method is proposed based on differential entropy and convolutional neural network for 4-class classification of motor imagery.EEG signals are extracted into 4 frequency bands(Alpha,Beta,Theta,and Gamma)through the filter,followed by the computation of differential entropy for each frequency band.According to the spatial characteristics of brain electrodes,the data structure is reconstructed into three-dimensional EEG signal feature cube which is input into convolutional neural network for 4-class classification.The method achieves an accuracy of 95.88%on the BCI Competition IV-2a public dataset.Additionally,a 4-class classification motor imagery dataset is established in the laboratory for the same processing,and an accuracy of 94.50%is obtained.The test results demonstrate that the proposed method exhibits superior recognition performance.