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 challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

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 challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

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.

Objective To analyze the effect of urinary ostomy bag on reduction of urinary tract infection in ICU patients with diarrhea.Methods A total of 445 diarrhea patients with indwelling catheter were admitted from June 2013 to May 2014,patients were divided into two groups according to the occurrence of diarrhea after admission,219 cases were in the observation group,226 cases were in the control group.The observation group used urinary tract ostomy bag connected with Kangwei anti-inverse drainage device,the control group used traditional methods of perianal care.Then,the incidence of urinary tract infection between two groups and the results of urine culture between patients of two groups with urinary tract infection were compared.Results The incidence of urinary tract infection in the observation group was 5.5％ (12/219),significantly lower than that of the control group,10.6％ (24/226),x2=3.952,P＜0.05.The urine culture results showed that 4 cases were intestinal strains in the observation group,and 17 cases in the control group,the difference was significant,x2=4.629,P＜0.05.Conclusions Urinary tract ostomy bag connected with Kangwei anti-inverse drainage device can effectively reduce the incidence of urinary tract infection in patients with diarrhea,it is simple to operate,and can reduce the workload of nurses and increase the comfort degree of patients,which is worthy of clinical application.