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Epilepsy detection is a critical medical task, but traditional methods face challenges in accuracy and reliability due to the difficulty of EEG data acquisition and the limitation of the number of sample seizures. To overcome these challenges, this paper proposes a new model for epilepsy detection that combines Graph Convolutional Neural Network (Graph Convolutional Network, GCN) and Transformer, aiming to significantly improve the accuracy and sensitivity of detection. The core of the model adopts GCN, which utilizes its powerful inter-node relationship capturing capability and graph feature learning mechanism. However, due to the limitation of traditional GCN in integrating global features, this model incorporates the Transformer structure to enhance global feature aggregation and reduce irrelevant feature interactions. After multiple rounds of testing of the GHB-MIT dataset, the model demonstrated excellent performance, with an average sensitivity of 92.97%, specificity of 94.60%, and accuracy of 94.59%, which was significantly better than the traditional method. Further comparison with the latest literature also confirms the advantages of the present method. In summary, the epilepsy detection model we developed based on graph convolutional neural network and Transformer not only shows significant improvement in accuracy and sensitivity, but also provides more accurate and reliable technical support for epilepsy diagnosis, which provides a valuable reference for research in related fields.

Photoacoustic (PA) imaging is a promising technology for functional imaging of biological tissues, offering optical contrast and acoustic penetration depth. However, the presence of signal aliasing from multiple PA sources within the same imaging object can introduce artifacts and significantly impact the quality of the PA tomographic images. In this study, an optimized method is proposed to suppress these artifacts and enhance image quality effectively. By leveraging signal time-frequency spectrum, signals from each PA source can be extracted. Subsequently, the images are reconstructed using these extracted signals and fused together to obtain an optimized image. To verify this proposed method, PA imaging experiments were conducted on two phantoms and two in vitro samples and the distribution relative error and root mean square error of the images obtained through conventional and optimized methods were calculated. The results demonstrate that the proposed method successfully suppresses the artifacts and substantially improves the image quality. [ABSTRACT FROM AUTHOR]

The timely assessment of muscle fatigue makes sense for reducing the risk of musculoskeletal injury during exercise. In general, muscle fatigue is accompanied by physiological changes. These changes affect the acoustic transmission properties of the skeletal muscles. This study investigated the variation of the acoustic transmission spectrum (ATS) of human upper arm muscles during sustained static contractions (SC). Based on the B-ultrasound image and radiofrequency (RF) ultrasonic echoes, we abstracted the RF echo signals from the subcutaneous fatty/fascia (SFF) and deep fascia/bone (DFB) interfaces. By dividing the echo spectrum of the DFB by the spectrum of the SFF, we obtained the ATS of the upper arm muscles. Then, by fitting the ATS with both the linear function (A(f) = af + b) and power-law function (A(f) = αƒβ), we analyzed the variations of a, b, α, and β along with the SC process of skeletal muscle. As muscle fatigue increases, the slope a decreases and the intercept b increases linearly; the α increases exponentially and β decreases linearly. In addition, the variation magnitude of ATS relates to the maximum voluntary contraction (MVC) force and the strength of the SC motion. These results suggest that a comprehensive analysis of ATS is a potential metric for assessing muscle fatigue. [ABSTRACT FROM AUTHOR]