Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

Background Venous thromboembolism (VTE) is a severe preventable complication among ophthalmic surgical patients. The knowledge, attitude, and practice (KAP) of nurses play a key role ineffective VTE prevention. However,little is known about the KAP of ophthalmic nurses’ prevention. This study aimed toexamine the level of KAPtoward VTE prevention among Chinese ophthalmic nurses, and to investigate the influencing factors of VTE practice. Design Cross-sectional study. Methods A total of 610 ophthalmic nurses from 17 cities in Hunan and Zhejiang Provinces, China, participated in our study. Data was collected via Sojump online platform from March to April 2021. A self-administered VTE questionnaire was developed to assess nurses' KAP toward VTE prevention. Multiple linear regression analysis was used to analyze the influencing factors of ophthalmicnurses’ VTE prevention practice. Results The scores (correct rates) of ophthalmic nurses’ knowledge, attitude, and practice were 103.87 ± 20.50 (76.4%), 21.96 ± 2.72, and 48.96 ± 11.23 (81.6%), respectively. The three lowest-scored knowledge items were related to VTE complications, physical prevention, and risk assessment. The three lowest-scored attitude items were related to nurses' training, VTE risk, and patient education. The three lowest-scored practice items were related to the assessment scale, VTE assessment, and patient education. Nurses’ knowledge, attitude, and practice were significantly correlated with each other. Multiple linear regression analysis showed that Hunan Province (B = 2.77, p = 0.006), general hospital (B = 2.97, p = 0.009), outpatient department (B = 3.93, p = 0.021), inpatient department (B = 2.50, p = 0.001), previous VTE prevention training (B = 3.46, p < 0.001), VTE prevention management in hospital (B = 4.93, p < 0.001), better knowledge (B = 0.04, p = 0.038), and positive attitude towards VTE prevention (B = 1.35, p < 0.001) were all significantly and positively associated with higher practice scores in VTE prevention. Conclusion Ophthalmic nurses generally have a satisfactory level of KAP in VTE prevention, but there is still room for improvement in certain areas. Nurses’ practice in VTE prevention was affected by environmental factors, training and management, knowledge and attitude, which may inform future intervention and education programs to improve VTE prevention.