In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.
BackgroundSince 1987, the Chinese government has promoted public mental health by continuously implementing mental health related policies. This research attempts to reveal the distribution and characteristics of mental health related policies. In addition, it can help stakeholders evaluate whether the environment for policy implementation has improved and identify key points in the development of the overall mental health system.MethodsWe used a bibliometric approach to analyze the evolution of mental health related policies in China from 1987 to 2020. A total of 239 mental health related policies were collected from Beida Fabao and official Internet websites of governmental departments. Co-wording, social networks, and citation analysis were applied to explore the evolutionary features of such policies.ResultsThe evolution of policy development showed that the number of mental health related policies in China has been increasing and their content has been enriched. Over time, mental health related policies not only gradually expanded its focus on common mental disorders, but also included an increasing number of keywords related to service provision, organization and administration. However, most policies were implemented independently by separate agencies and the number of policies jointly implemented by different agencies only accounted for 32.64% of all the policies implemented. The Ministry of Health (MOH) is at the core of the collaborative network associated with implementing mental health related policies in China.ConclusionThe environment associated with the implementation of mental health related policies in China is gradually improving. However, cross-sector collaboration among different agencies needs to be strengthened and financial support for related resources needs more attention. A clear division of responsibilities among various agencies and a sustainable financing mechanism are essential to the development and implementation of mental health related policies.
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