ABSTRACT
The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
Subject(s)
Humans , Adult , Imagination , Neural Networks, Computer , Imagery, Psychotherapy/methods , Electroencephalography/methods , Algorithms , Brain-Computer Interfaces , Signal Processing, Computer-AssistedABSTRACT
Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
Subject(s)
Humans , Electroencephalography , Intelligence , Parkinson Disease/diagnosis , Polysomnography , REM Sleep Behavior Disorder/diagnosisABSTRACT
Objective To investigate the clinical efficacy and influencing factors of arterial switch operation (ASO) in the treatment of complete transposition of great arteries (TGA).Methods Totally 156 children with TGA who underwent ASO surgery from January 2005 to December 2011 were selected as the subjects.The clinical curative effect and prognosis of all patients were observed,and the relationship be tween clinical features and prognosis was analyzed.Results Totally 156 cases of TGA children were successfully completed the operation,and 29 patients died during the 5 year follow-up period,the mortality rate was 18.59％.Univariate analysis showed that the death in children with TGA after ASO was closely related to coronary artery abnormality,cardiopulmonary bypass time,aortic occlusion time,postoperative low cardiac output syndrome and reoperation (P ＜ 0.05).Multivariate logistic regression analysis showed that coronary artery abnormalities,and low cardiac output syndrome were independent outcome factors leading to postoperative death in patients.Conclusions The postoperative death of ASO in TGA children is closely related to the incidence of coronary artery abnormalities,the time of cardiopulmonary bypass,the time of oc clusion of aorta and the occurrence of postoperative low cardiac output syndrome,which should be pay attention to and take relevant measures.
ABSTRACT
Objective To observe the expression of caspase-3 on the trehalose as cryoprotectant for preserving aortic valve homograft in liquid nitrogen.Methods The aortic valve homograft was divided into 5groups,namely:0.1 mol/L DMSO(control group),0.1 mol/L trehalose(experimental group 1),0.1 mol/L trehalose+0.1 mol/L DMSO(experimental group 2),0.2 mol/L trehalose+0.1 mol/L DMSO(experimental group 3),0.3 mol/L trehalose+0.1 mol/L DMSO(experimental group4).At the time of 12 months,15 months and 18 months when preserved in liquid nitrogen,relative expression of caspase-3 of the aortic valve homograft was measured by RT-PCR and Western Blot.Fresh group was a negative control group.Results At the same time(P<0.05),the expression of caspase-3 of fresh aortic tissue was slightest.The experimental group 2 was in accord with the experiment group 3,which was of a sort compare with the fresh group.The experimental group 4,which was worse than the experimental group 2 and 3,ranked above the experimental group 1.The worst was the control group.Conclusions The joint use of trehalose and DMSO could well inhibit the expression of caspase-3.Moreover.0.1mol/L trehalose+0.1 mol/L DMSO and 0.2 mol/L trehalose +0.1 mol/L DMSO could maximize the inhibition of the expression of caspase-3.