Objective To investigate the association between types of rapid eye movements during sleep and ICD-10 as a mothod for diagnosing depression. Methods Depression was diagnosed according to ICD-10 and changes of 9 variables of REM sleep in 120 psychiatric outpatients and inpatients by calculating the Kappa values. Results In 120 psychiatric patients, 52 depressive patients were diagnosed by using ICD-10, and 58 patients were diagnosed as depression according to decreased REM sleep latency and increased REM activity, REM intensity, REM density, and increasing amount of REM sleep. Kappa value was 0.73, Plt;0.05, which implied that these two diagnosing ways were significantly consistent. Conclusions Measuremen of REM sleep variable should be investigated further as a supplementary method for diagnosing depression.
ObjectiveTo identify the clinical significance of epileptic eye movement for localization of posterior epileptic seizures. MethodsThere were 12 posterior epileptic patients, who were undertaken the detailed presurgical evaluation and ictal SEEG recording, and the epileptogenic zone of whom was confirmed restricted within posterior cortex through the boarder of epileptic cortical resection and the result of epileptic surgery, included in the research. ResultsThe cluster analysis of posterior cortical area of interest showed that parieto-occipital sulcus (POS) with the adjacent cortical areas belonged to the Medial Group; intraparietal sulcus (IPS) with the adjacent areas belonged to the Intermediate Group; and the Lateral Group included anterior occipital sulcus (AOS) and posterior temporal cortex. Eyes forced stare had the significant correlation with IPS, POS and related cortical group. Contraversive eye deviation had no significant correlation with cortical eye field with the related cortical areas. ConclusionsForced eye stare was significant eye movement in posterior epilepsy, and had significant correlation with IPS and POS. Ipsiversive eye deviation and convergence binocular movement were rare semiology in posterior epilepsy, and AOS had significant correlation with the two type of epileptic eyes movement.
The aim of this study is to investigate the search time regulation of objectives and eye movement behavior characteristics in the multi-objective visual search. The experimental task was accomplished with computer programming and presented characters on a 24 inch computer display. The subjects were asked to search three targets among the characters. Three target characters in the same group were of high similarity degree while those in different groups of target characters and distraction characters were in different similarity degrees. We recorded the search time and eye movement data through the whole experiment. It could be seen from the eye movement data that the quantity of fixation points was large when the target characters and distraction characters were similar. There were three kinds of visual search patterns for the subjects including parallel search, serial search, and parallel-serial search. In addition, the last pattern had the best search performance among the three search patterns, that is, the subjects who used parallel-serial search pattern spent shorter time finding the target. The order that the targets presented were able to affect the search performance significantly; and the similarity degree between target characters and distraction characters could also affect the search performance.
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.
The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users’ experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.