Developement of Matlab-based Graphical User Interface (GUI) for detection of high frequency oscillations (HFOs) in epileptic patients (original) (raw)
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EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy
Frontiers in Neuroinformatics, 2018
The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias, and computational time. In this manuscript, we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space magnetoencephalographic (MEG) data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.
Electroencephalogram (EEG) is a medical imaging technique that records the electrical activity in the brain. Epilepsy, the most common neurological disorder characterized by sudden and recurrent neuronal firing in the brain can be detected by analyzing EEG of the subject. Electroencephalography (EEG) measures the electrical activity of the brain and represents a summation of post-synaptic potentials from a large number of neurons. Over a past few decades many researches all over the world, focusing and working to automate the analysis of EEG signals to identify and categorized the diseases. In this paper, we present a review of the significant researches associated with the automated detection of epileptic seizures and brain tumor using EEG signals.
A build up of seizure prediction and detection Software: A review
Journal of Clinical Images and Medical Case Reports, 2021
Introduction: Neurological diseases are much often due to our stressed daily life, and epilepsy is considered as a second cause of hospitalization in neurological illness. It is about 30% of epileptic cases where medicine would not stop or control seizure; hence, a surgical intervention is required to delineate abnormal hyperexcitable cortical tissue. Defining these epileptogenic zones is a challenge that require physiological and anatomical acquisition. Discussion: Clinicians, researcher and engineer researcher are multiplying advanced techniques in order to exploit these acquisitions for a better diagnosis. Several software are used to enhance epilepsy diagnosis. Here we proposed a software that rely on spacetime evolution of inter- ictal gamma oscillations. Conclusion: Our proposed software would predict a build up of seizure during monitoring of stereo-electroencephalographic SEEG recording. It allows also detection of seizure during analysis and diagnosis of SEEG. This software...
Automated seizure detection: Unrecognized challenges, unexpected insights
Epilepsy & Behavior, 2011
One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility, three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of parameters change of a 2 nd order auto-regressive model, another is based on the Wavelet Transform Maximum Modulus for quantification of changes in the logarithm of the standard deviation of ECoG power and yet another employs the ratio of short-to-long term averages computed from cortical signals. The central finding, fluctuating concordance among all methods' output as a function of seizure duration, uncovers unexpected hurdles in the path to a universal definition, while furnishing relevant knowledge in the dynamical (spectral non-stationarity and varying ictal signal complexity) and clinical (probable attainability of consensus) domains. Highlights Consensus among epileptologists as to what grapho-elements are classifiable as ictal, is difficult to achieve. Adoption of a universal seizure definition would be of heuristic value. Four signal processing methods were applied to a seizure time seizures to identify ictal markers. Concordance among the various methods for key metrics such as sensitivity, specificity and speed of detection varied as a function of seizure duration. Discordance among methods hints at fluctuating complexity/entropy of ictal signals and potential unattainability of a universal, internally consistent seizure definition.
Improved Patient-Independent System for Detection of Electrical Onset of Seizures
Journal of Clinical Neurophysiology, 2019
Purpose: To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy. Methods: We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system. Results: Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively. Conclusions: The support vector machine-based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers. Conclusions: Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.
Automated diagnosis of epilepsy using EEG power spectrum
…, 2012
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video-EEG monitoring. The patient population was diagnostically diverse with 87 diagnosed with either generalized or focal seizures. The remainder was diagnosed with non-epileptic seizures. The sensitivity was 92% (95% CI: 85-97%) and the negative predictive value was 82% (95% CI: 67%-92%). We discuss how these findings suggest that this CAD can be used to supplement event-based analysis by trained epileptologists.