Niederhauser's model for epilepsy and wavelet methods
J. P. Trevino, V. H. Castillo, H. C. Rosu, J. L. Moran, J. S. Murguia
Abstract
Wavelets and wavelet transforms (WT) could be a very useful tool to analyze electroencephalogram (EEG) signals. To illustrate the WT method we make use of a simple electric circuit model introduced by Niederhauser, which is used to produce EEG-like signals, particularly during an epileptic seizure. The original model is modified to resemble the 10-20 derivation of the EEG measurements. WT is used to study the main features of these signals
Create a lesson
Related papers
Unimodality and Radial Monotonicity of the Magnetic Resonance Fingerprinting T1/T2 Matching Objective
Ze Wang
A Reconfigurable Pipelined-SAR ADC with Embedded Compression for Temporal Compressed-Sensing Ultrasound Imaging
Reza Pakdaman Zangabad, Xitie Zhang, Levent Degertekin et al.
Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting
Wen Li, Yan Dai, Arely Perez Rodriguez et al.
Constrained estimation of rotational invariants of the cumulant expansion (RICE) for rapid tensor-valued diffusion MRI
Jinyang Yu, Oliver Gödicke, Frederik B. Laun et al.
Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty et al.
Sparse Delta Integration method for the calculation of spatiotemporal pressure fields of arbitrary ultrasound transducer geometries
Deyver E. Rivera, Charlie Demene, Mickael Tanter