We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg-Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1142/S0219635216500242 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Investigation of the mechanical characteristics of porcine brain tissue in complex environments.
Acta Bioeng Biomech
June 2024
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
: Brain tissue immersed in cerebrospinal fluid often exhibits complex mechanical behaviour, especially the nonlinear stress- strain and rate-dependent responses. Despite extensive research into its material properties, the impact of solution environments on the mechanical behaviour of brain tissue remains limited. This knowledge gap affects the biofidelity of head modelling.
View Article and Find Full Text PDFImproving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.
J Chem Inf Model
January 2025
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves.
View Article and Find Full Text PDFPolarization-Independent High-Q Phase Gradient Metasurfaces.
Nano Lett
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Dielectric metasurfaces have emerged as an unprecedented platform for precise wavefront manipulation at subwavelength scales with nearly zero loss. When aiming at dynamic applications such as AR/VR and LiDAR, high-quality factor (high-Q) phase gradient metasurfaces have emerged as a way to boost weak light-material interactions in flat-optical components. However, resonant features are naturally tied to polarization, limiting devices to operating on a single polarization state, which reduces the efficiency and adaptability of wave-shaping.
View Article and Find Full Text PDFOptimizing photovoltaic power plant forecasting with dynamic neural network structure refinement.
Sci Rep
January 2025
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction.
View Article and Find Full Text PDFDiscovering the climate dependent disease transmission mechanism through learning-explaining framework.
J Theor Biol
January 2025
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaan Xi, 710049, PR China. Electronic address:
There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!