ANALYSIS OF ABSENCE SEIZURE GENERATION USING EEG SPATIAL-TEMPORAL REGULARITY MEASURES

2012 ◽  
Vol 22 (06) ◽  
pp. 1250024 ◽  
Author(s):  
NADIA MAMMONE ◽  
DOMENICO LABATE ◽  
AIME LAY-EKUAKILLE ◽  
FRANCESCO C. MORABITO
Keyword(s):  
Healthy Subjects ◽  
Dynamical Behavior ◽  
Epileptic Seizures ◽  
Temporal Analysis ◽  
Extensive Study ◽  
Permutation Entropy ◽  
State Transitions ◽  
Gradual Transition ◽  
Absence Seizure ◽  
Two Parameters

Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

scholarly journals Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicolette Driscoll ◽  
Richard E. Rosch ◽  
Brendan B. Murphy ◽  
Arian Ashourvan ◽  
Ramya Vishnubhotla ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Spatial Scales ◽  
Epileptic Seizures ◽  
Therapeutic Interventions ◽  
State Transitions ◽  
Integrated Analysis ◽  
High Temporal Resolution ◽  
Seizure Onset ◽  
Acute Seizures

AbstractNeurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.

scholarly journals Singular perturbations of the unicritical polynomials with two parameters

2016 ◽  
Vol 37 (6) ◽  
pp. 1997-2016 ◽  
Author(s):  
YINGQING XIAO ◽  
FEI YANG
Keyword(s):  
Julia Set ◽  
Dynamical Behavior ◽  
Rational Maps ◽  
Topological Properties ◽  
Fatou Set ◽  
The Family ◽  
Image Position ◽  
Two Parameters ◽  
Unicritical Polynomials

In this paper, we study the dynamics of the family of rational maps with two parameters $$\begin{eqnarray}f_{a,b}(z)=z^{n}+\frac{a^{2}}{z^{n}-b}+\frac{a^{2}}{b},\end{eqnarray}$$ where $n\geq 2$ and $a,b\in \mathbb{C}^{\ast }$. We give a characterization of the topological properties of the Julia set and the Fatou set of $f_{a,b}$ according to the dynamical behavior of the orbits of the free critical points.

Nonlinear Dynamical and Statistical Approaches to Investigate State Transitions before Epileptic Seizures

2004 ◽  
pp. 239-249 ◽  
Author(s):  
D.-S. Shiau ◽  
W. Chaovalitwongse ◽  
L. D. Iasemidis ◽  
P. M. Pardalos ◽  
P. R. Carney ◽  
...  
Keyword(s):  
Epileptic Seizures ◽  
State Transitions ◽  
Nonlinear Dynamical ◽  
Statistical Approaches

scholarly journals Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 102
Author(s):  
Michele Lo Giudice ◽  
Giuseppe Varone ◽  
Cosimo Ieracitano ◽  
Nadia Mammone ◽  
Giovanbattista Gaspare Tripodi ◽  
...  
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Epileptic Seizures ◽  
Permutation Entropy ◽  
Diagnostic Tools ◽  
Support Vector ◽  
Feature Maps ◽  
Time Frequency ◽  
Interictal Eeg

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.

scholarly journals A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7972
Author(s):  
Jee S. Ra ◽  
Tianning Li ◽  
Yan Li
Keyword(s):  
Sensitivity And Specificity ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Optimization Method ◽  
Channel Selection ◽  
Permutation Entropy ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

scholarly journals Absence Seizure Detection Algorithm for Portable EEG Devices

2021 ◽  
Vol 12 ◽  
Author(s):  
Pawel Glaba ◽  
Miroslaw Latka ◽  
Małgorzata J. Krause ◽  
Sławomir Kroczka ◽  
Marta Kuryło ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Cost ◽  
Clinical Manifestations ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
Absence Epilepsy ◽  
Absence Seizure ◽  
Epileptiform Discharges ◽  
Morlet Continuous Wavelet Transform

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

RANDOM DISPERSAL IN A PREDATOR-PREY-PARASITE SYSTEM

2010 ◽  
Vol 18 (04) ◽  
pp. 825-845 ◽  
Author(s):  
SOPHIA R.-J. JANG ◽  
JAMES BAGLAMA ◽  
LI WU
Keyword(s):  
Steady State ◽  
Dynamical Behavior ◽  
Homogeneous System ◽  
Reproductive Number ◽  
Predator Prey ◽  
Random Dispersal ◽  
Prey Interaction ◽  
Asymptotic Dynamics ◽  
Two Parameters ◽  
Infected Prey

We propose predator-prey-parasite models to study the effects of parasites upon the predator-prey interaction. There are two parameters that are used to model the effectiveness of the infected prey and infected predator. For the spatial homogeneous system, the asymptotic dynamics depend on the reproductive number of the parasite. The parasite can persist in the population if this reproductive number is larger than one. Numerical simulations suggest that less competitiveness of the infected predator can make the predator-prey interaction less stable. The dynamics may move from coexisting steady state to oscillations. For the spatial heterogeneous system, diffusion may destabilize the homogeneous interior steady state for a particular set of diffusion coefficients. However, both systems do not exhibit complicated dynamical behavior.

Metatarsal Loading During Gait—A Musculoskeletal Analysis

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Amir A. Al-Munajjed ◽  
Jeffrey E. Bischoff ◽  
Mehul A. Dharia ◽  
Scott Telfer ◽  
James Woodburn ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Longitudinal Direction ◽  
Extensive Study ◽  
Detailed Knowledge ◽  
Dynamic Simulations ◽  
Inverse Dynamic ◽  
Metatarsal Bones ◽  
First Ray ◽  
The Individual ◽  
Joint Loads

Detailed knowledge of the loading conditions within the human body is essential for the development and optimization of treatments for disorders and injuries of the musculoskeletal system. While loads in the major joints of the lower limb have been the subject of extensive study, relatively little is known about the forces applied to the individual bones of the foot. The objective of this study was to use a detailed musculoskeletal model to compute the loads applied to the metatarsal bones during gait across several healthy subjects. Motion-captured gait trials and computed tomography (CT) foot scans from four healthy subjects were used as the inputs to inverse dynamic simulations that allowed the computation of loads at the metatarsal joints. Low loads in the metatarsophalangeal (MTP) joint were predicted before terminal stance, however, increased to an average peak of 1.9 times body weight (BW) before toe-off in the first metatarsal. At the first tarsometatarsal (TMT) joint, loads of up to 1.0 times BW were seen during the early part of stance, reflecting tension in the ligaments and muscles. These loads subsequently increased to an average peak of 3.0 times BW. Loads in the first ray were higher compared to rays 2–5. The joints were primarily loaded in the longitudinal direction of the bone.

Characterizing dynamics of absence seizure EEG with spatial-temporal permutation entropy

2018 ◽  
Vol 275 ◽  
pp. 577-585 ◽  
Author(s):  
Ke Zeng ◽  
Gaoxiang Ouyang ◽  
He Chen ◽  
Yue Gu ◽  
Xianzeng Liu ◽  
...  
Keyword(s):  
Permutation Entropy ◽  
Absence Seizure

scholarly journals Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness

2021 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Matthias Kreuzer ◽  
Tobias Kiel ◽  
Leonie Ernst ◽  
Marlene Lipp ◽  
Gerhard Schneider ◽  
...  
Keyword(s):  
General Anesthesia ◽  
Dynamic State ◽  
Permutation Entropy ◽  
State Transitions ◽  
Emergence Period ◽  
Emergence Patterns ◽  
Processed Eeg ◽  
Inhalational Anesthetic ◽  
Cortical Depression ◽  
State Index

Purpose: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). Methods: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. Results: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the “wake” CSI values that occurred minutes before ROR in the inhalational anesthetic groups. Conclusion: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.

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