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Abstract Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time–frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients’ lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

Background Previous studies have found that 25%-85% of stroke patients develop post-stroke fatigue (PSF) , and continuous feeling of fatigue seriously affects patients' daily life and rehabilitation process. Therefore, it is urgent to understand the psychological experience and needs of such patients. However, the results of previous single qualitative studies may not be representative. Objective To systematically review and integrate qualitative studies on the fatigue experience of patients with PSF, so as to provide a reference for the construction of management strategies for PSF patients. Methods PsycINFO, PubMed, Web of Science, CINAHL, Cochrane Library, Scopus, Joanna Briggs Institute (JBI) Evidence-Based Health Care database, CNKI, Wanfang Data, VIP, CBM were searched by computer to screen qualitative studies on fatigue experience of patients with PSF from inception to July 5, 2023. JBI Critical Appraisal Tool for qualitative studies in Australia was used to evaluate the quality of included literature, and Meta-synthesis was performed to integrate results. Results A total of 10 papers were included, and 22 primary outcomes were distilled, grouped into 8 categories, and synthesized into 4 integrated outcomes, including significant lack of cognition of PSF; intricate negative physical and psychological experience; different self-regulation of PSF and multiple needs for PSF rehabilitation. Conclusion PSF patients have insufficient cognition of fatigue with significant negative physical and psychological experience. Therefore, PSF patients should be identified and screened at an early stage with strengthened fatigue education and personalized fatigue management strategies. Emphasis should also be placed on the role of community and family in the management of fatigue, so that they can work together with healthcare professionals to provide a favorable rehabilitation environment for PSF patients.