scholarly journals Prediction of Epileptic Seizures based on Mean Phase Coherence

2017 ◽  
Author(s):  
Gorish Aggarwal ◽  
Tapan K. Gandhi
Keyword(s):  
Phase Synchronization ◽  
Epileptic Seizures ◽  
Phase Coherence ◽  
Test Cases ◽  
Eeg Signal ◽  
Seizure Attack ◽  
Prediction Horizon ◽  
Ictal Eeg ◽  
Eeg Data ◽  
Reliable Mechanism

AbstractAround 1-2 % of the world's population suffers from epilepsy and 20 % of that vulnerable mass can't be cured through surgery or medicine. Epileptic seizures often occur unpredictably and may cause serious damage to the patient in adverse situations, for e.g. getting a seizure while driving or crossing a road can be fatal. In such a scenario, a reliable mechanism to predict the onset of seizure beforehand is much desirable. In this study, A reliable real-time technique for prediction of epileptic seizures is presented. Mean Phase Coherence (MPC), as a measure of phase synchronization, is used to predict impending seizures in a multi-channel EEG data. It was found that during the pre-ictal stages, MPC values and thus phase synchronization between various channels was found to drop significantly below the level in a non-ictal EEG signal. The range of the prediction horizon for seizures varied from 4-10 mins and prediction of impending seizure attack is possible for 8 out of 10 test cases.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

SIMULATION AND MODELING OF EVOKED RESPONSE ELECTROENCEPHALOGRAPH SIGNAL

1999 ◽  
Vol 10 (04) ◽  
pp. 759-776
Author(s):  
D. R. KULKARNI ◽  
J. C. PARIKH ◽  
R. PRATAP
Keyword(s):  
Small Range ◽  
Eeg Signal ◽  
Simulation And Modeling ◽  
State Space Reconstruction ◽  
Eyes Closed ◽  
Normal Individuals ◽  
Eeg Data ◽  
Closed Data ◽  
Simulation Results ◽  
Low Dimensional

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength in α waves decreases whereas that in β wave increases. We also carried out simulations separately and in combination for δ, θ, α and β waves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.

scholarly journals An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed I. Sharaf ◽  
Mohamed Abu El-Soud ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Feature Selection ◽  
Correlation Energy ◽  
Epileptic Seizures ◽  
Compact Set ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Computer Aided ◽  
Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

scholarly journals Temporal Epileptogenesis: Localizing Value of Scalp and Subdural Interictal and Ictal EEG Data

Epilepsia ◽  
2001 ◽  
Vol 42 (4) ◽  
pp. 508-514 ◽  
Author(s):  
Warren T. Blume ◽  
Giannina M. Holloway ◽  
Samuel Wiebe
Keyword(s):  
Ictal Eeg ◽  
Eeg Data

scholarly journals The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7711
Author(s):  
Ilona Karpiel ◽  
Zofia Kurasz ◽  
Rafał Kurasz ◽  
Klaudia Duch
Keyword(s):  
Gold Standard ◽  
Healthy Subjects ◽  
Data Interpretation ◽  
Eeg Signal ◽  
Sensorimotor Area ◽  
Eeg Data ◽  
Reduction Methods ◽  
Characteristic Values ◽  
No Gold Standard ◽  
Correct Data

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area—the sensorimotor area.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

Reevaluation of the Critically Ill Patients With Nonconvulsive Status Epilepticus by Using Salzburg Consensus Criteria

2018 ◽  
Vol 49 (6) ◽  
pp. 425-432 ◽  
Author(s):  
Ozlem Gungor Tuncer ◽  
Ebru Altindag ◽  
Sevda Ozel Yildiz ◽  
Mecbure Nalbantoglu ◽  
Mehmet Eren Acik ◽  
...  
Keyword(s):  
Status Epilepticus ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Laboratory ◽  
Laboratory Data ◽  
Real Life ◽  
Nonconvulsive Status Epilepticus ◽  
Life Evaluation ◽  
Ictal Eeg ◽  
Eeg Data

Objective. We aimed to assess the usefulness of the Salzburg Consensus Criteria (SCC) for determining the prognosis of critically ill patients with nonconvulsive status epilepticus (NCSE). Methods. We retrospectively reviewed consecutive patients with unconsciousness followed up in the intensive care unit (ICU). Three clinical neurophysiologists, one of them blinded to clinical and laboratory data, reevaluated all EEG data independently and determined NCSE according to SCC. The incidence of NCSE and ictal EEG patterns and their relationship to clinical, laboratory, neuroradiological, and prognostic findings were assessed. Results. A total of 107 consecutive patients with mean age 68.2 ± 15.3 years (57 females) were enrolled in the study. Primary neuronal injury was detected in 59 patients (55.7%). Thirty-three patients (30.8%) were diagnosed as NCSE. While authors decided to treat 33 patients (30.8%), 32 patients (29.9%) had been treated in real-life evaluation. Clinical and EEG improvement were detected in 12 patients (11.3%) in real-life treatment group showing correlation with lack of intubation and ICU stay related to postsurgical event. Rate of mortality (45.8%) was high showing association with systemic-metabolic etiology, severity of coma and presence of “plus” modifiers in the EEG. Conclusion and Significance. Our findings suggest that SCC is highly compatible with clinical practice in the decision for treatment of patients with NCSE. The presence of “plus” modifiers in the EEG was found to be associated with mortality in these patients and was a significant marker for the high mortality rate.

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