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Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations.Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ).Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70–0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65–0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001).Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.

The application of the Internet of Things technology in intelligent mines has become an important research area with the rapid development of the cloud platform and Internet +. The Internet of Things technology is a core technology that specializes in model building and algorithm implementation for the application of the mine Internet of Things. The framework of the mine Internet of Things integrated information platform from the perception layer, the transmission layer, the data layer, and the application layer is constructed according to the complexity and particularity of the underground working environment in this paper, which establishes the technical flow of the Internet of Things for underground emergency hedging subsystems and designs the main functions of five subsystems based on the Internet of Things technology. This paper proposes the structure and function of the underground emergency hedging system and introduces key technology combined with the Internet of Things technology. A novel algorithm for underground geomagnetic GRPM positioning and a mathematical model of the underground emergency hedging path are proposed according to the concepts of spatial data mining and the intelligent mine, which verify the validity of the model in intelligent decision-making in the simulation test. The simulation results show that the algorithm and model provided an effective idea for positioning and hedging in an underground emergency and help improve the intelligent analysis of the mine Internet of Things data.