The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.
The electroencephalogram (EEG) or scalp recordings of brain field potentials continues to be an attractive tool in clinical practice due to its noninvasiveness and its real-time depiction of brain function. In brain tumor diagnostics, EEG is most relevant in assessing how basic functionality is affected by the lesion and how the brain responds to treatments (e.g. post-operative). This paper focuses on developing an automated system to identify space-occupying lesions in the brain using EEG signals. We discuss major complications in relating EEG to different tumor classes and suggest an approach of feature extraction using wavelet techniques and classification by self-organizing maps. Initial tests show improvement over conventional frequency band features common in the EEG community. The tests also highlight the need to obtain efficient physically-motivated features as to how EEG is affected by various tumors.
Fetal heart rate discrimination is an evolving field in biomedical engineering with many efforts dedicated to avoid preterm deliveries by way of improving fetus monitoring methods and devices. Entropy analysis is a nonlinear signal analysis technique that has been progressively developed to improve the discriminability of a several physiological signals, with Kernel based entropy parameters (KBEPs) found advantageous over standard techniques. This study is the first to apply KBEPs to analyze fetal heart rates. Specifically, it explores the usability of the cutting-edge nonlinear KBEPs in discriminating between healthy fetuses and fetuses under distress. The database used in this study comprises 50 healthy and 50 distressed fetal heart rate signals with severe intrauterine growth restriction. The Cascade analysis investigates six kernel based entropy measures on fetal heart rates discrimination, and compares them to four standard entropies. The study presents a statistical evaluation of the discrimination power of each parameter (paired t-test statistics and distribution spread). Simulation results showed that the distribution ranges in 80% of the entropy parameters in the distressed heart group are higher than those in the healthy control group. Moreover, the results show that it is advantageous to choose Circular entropy then Cauchy entropy (p < 0.001) over the standard techniques, in order to discriminate fetal heart rates.
The cochlea is an indispensable preliminary processing stage in auditory perception that employs mechanical frequency-tuning and electrical transduction of incoming sound waves. Cochlear mechanical responses are shown to exhibit active nonlinear spatiotemporal response dynamics (e.g., otoacoustic emission). To model such phenomena, it is often necessary to incorporate cochlear fluid-membrane interactions. This results in both excessively high-order model formulations and computationally intensive solutions that limit their practical use in simulating the model and analyzing its response even for simple single-tone inputs. In order to address these limitations, the current work employs a control-theoretic framework to reformulate a nonlinear two-dimensional cochlear model into discrete state space models that are of considerably lower order (factor of 8) and are computationally much simpler (factor of 25). It is shown that the reformulated models enjoy sparse matrix structures which permit efficient numerical manipulations. Furthermore, the spatially discretized models are linearized and simplified using balanced transformation techniques to result in lower-order (nonlinear) realizations derived from the dominant Hankel singular values of the system dynamics. Accuracy and efficiency of the reduced-order reformulations are demonstrated under the response to two fixed tones, sweeping tones and, more generally, a brief speech signal. The corresponding responses are compared to those produced by the original model in both frequency and spatiotemporal domains. Although carried out on a specific instance of cochlear models, the introduced framework of control-theoretic model reduction could be applied to a wide class of models that address the micro- and macro-mechanical properties of the cochlea.
International audience—Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of seizures in EEG signs. Towards this aim, nonlinear tools are strongly recommended to select the seizure-sensitive features prior to classification. However, the choice of the feature remains challenging. With the multitude of entropy parameters available in literature, and in order to perform a judicious selection of features that are fed to classifiers, this paper presents a comparative study of a host of candidate promising feature extraction techniques. Four entropy features namely Approximate Entropy, Sample Entropy and Renyi entropy of order 2 and Renyi entropy of order 3, were implemented as the standard techniques. Three kernel-based features namely Triangular Entropy, Spherical Entropy and Cauchy entropy were implemented. The former and latter entropies were computed from EEG recordings during induced seizures in three distinct phases: the pre-ictal (pre-seizure) phase, the ictal (seizure) phase, and the post-ictal (post-seizure) phase. Results showed that, among kernel-based methods, Spherical entropy features exhibited the largest parameter sensitivity to (Seizure-Normal) phase changes with the highest normalized relative separation (100%). The sample entropy feature in turn showed the most sensitive to EEG phase changes with the highest relative separation (94.85%), among the studied entropy alternatives
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