AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection.

Objective.Depression is a common chronic mental disorder characterized by high rates of prevalence, recurrence, suicide, and disability as well as heavy disease burden. An accurate diagnosis of depression is a prerequisite for treatment. However, existing questionnaire-based diagnostic methods are limited by the innate subjectivity of medical practitioners and subjects. In the search for a more objective diagnostic methods for depression, researchers have recently started to use deep learning approaches.Approach.In this work, a deep-learning network, named adaptively multi-time-window graph convolutional network (GCN) with long-short-term memory (LSTM) (i.e. AMGCN-L), is proposed. This network can automatically categorize depressed and non-depressed people by testing for the existence of inherent brain functional connectivity and spatiotemporal features contained in electroencephalogram (EEG) signals. AMGCN-L is mainly composed of two sub-networks: the first sub-network is an adaptive multi-time-window graph generation block with which adjacency matrices that contain brain functional connectivity on different time periods are adaptively designed. The second sub-network consists of GCN and LSTM, which are used to fully extract the innate spatial and temporal features of EEG signals, respectively.Main results.Two public datasets, namely the patient repository for EEG data and computational tools, and the multi-modal open dataset for mental-disorder analysis, were used to test the performance of the proposed network; the depression recognition accuracies achieved in both datasets (using tenfold cross-validation) were 90.38% and 90.57%, respectively.Significance.This work demonstrates that GCN and LSTM have eminent effects on spatial and temporal feature extraction, respectively, suggesting that the exploration of brain connectivity and the exploitation of spatiotemporal features benefit the detection of depression. Moreover, the proposed method provides effective support and supplement for the detection of clinical depression and later treatment procedures.

Effectiveness of health education on iodine deficiency disorders and drinking-water-borne endemic fluorosis in Xinzhou City Shanxi Province in 2011 / 中国地方病学杂志

Objective To find out whether people in Xinzhou Shanxi know the hazard of both iodine deficiency disorders and drinking-water-borne endemic tluorosis,in Xinzhou Shanxi and to promote people actively participate in prevention of the diseases and to achieve a sustainable development of health education in endemic diseases.Methods Ningwu,Baode,Kelan and Jingle Counties were selected to carry out the.health education program of iodine deficiency disorders and Wutai,Xinfu and Fanshi counties were selected to carry out the health education project of drinking-water-borne endemic fluorosis in Xinzhou City Shanxi Province in 2011.To carry out the base line questionnaire survey,in the beginning and at the end of the project,three townships from each county were chosen,and one primary school was selected from each chosen township,15 housewives were selected from each chosen township and 30 fifth grade students were selected from each primary school.Results ① The baseline survey:a total of 366 pupils of grade 5 and 183 housewives were investigated,and the awareness rate of iodine deficiency disorders was 77.41％ (850/1098) and 80.33％ (441/549),respectively; a total of 270 pupils of grade 5 and 138 housewives were investigated,and the awareness rate of drinking-water-borne endemic fluorosis was 80.74％ (654/810) and 81.40％ (337/414),respectively; ② The effect evaluation:a total of 366 pupils of grade 5 and 181 housewives were investigated,and the awareness rates of iodine deficiency disorders were 91.62％ (1006/1098) and 92.45％ (502/543),which were compared with that of baseline investigation,and the awareness rates were improved significantly (x2 =84.69,34.04,all P ＜ 0.01); a total of 270 pupils of grade 5 and 138 housewives were investigated,awareness rates of drinking-water-borne endemic fluorosis were 93.95％ (761/810) and 93.48％(387/414),which were compared with that of baseline survey,and the awareness rates were improved significantly(x2 =63.94,27.47,all P ＜ 0.01).Conclusions After implementation of health education project on endemic diseases,the self-protection awareness of the fifth grade students and housewives is promoted effectively,awareness of prevention knowledge on endemic diseases is raised,which lays a foundation for controlling and eliminating the endemic diseases.

Fuzzy support vector machines for adaptive Morse code recognition.

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.