Classification Of Epileptic Seizures Research Articles

Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference.

A high number of patients with epileptic seizures (ES) are misdiagnosed due to prevalence of mimic conditions. The clinical characteristics of mimics are often similar to ES. The events mostly misdiagnosed are of psychogenic origin and are termed as psychogenic non-epileptic seizures (PNES). The gold standard for diagnosis of PNES is video-electroencephalography monitoring (VEM), which is a resource demanding process. Hence, need for a more object method of PNES diagnosis is created. Accelerometer sensors have been used previously for the diagnosis of ES. In this work, we present a new approach for detection and classification of PNES using wrist-worn accelerometer device. Various time, frequency and wavelet space features are extracted from the accelerometry signal. Feature compression is then performed using Fisher linear discriminant analysis (FLDA). A Bayesian classifier is then trained using kernel estimator method. The algorithm was trained and tested on data collected from 16 patients undergoing VEM. When tested, the algorithm detected all seizures with 20 false alarms and correctly classified 100% PNES and 75% ES, respectively of the detected seizures.

Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms

Background: Classification and localization of focal epileptic seizures provide a proper diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long-term electroencephalography (EEG) is tedious, time-consuming and leads to human error. Therefore, there is a need for an automated classification system.Methods: In this paper, we introduce a tool called CADFES: computerized automated detection of focal epileptic seizures. For the study, total 41.66 hours of EEG data from the Bern-Barcelona database was used. Set of 28 features were extracted from time, frequency, and statistical domain and significant features were selected using neighborhood component analysis (NCA). In NCA, optimization of regularization parameter ensured better classification accuracy (less classification loss) with seven features. The performance of the algorithm was assessed using support vector machine (SVM), K-nearest neighbor (K-NN), random forest and adaptive boosting (AdaBoost) classifiers.Results: Experimental results revealed sensitivity, specificity, accuracy, positive predictive rate, negative predictive rate, and area under the curve of 97.6%, 94.4%, 96.1%, 92.9%, 98.8% and 0.96 respectively using the SVM classifier. Finally, MATLAB based software tool referred to as CADFES was introduced for automated classification of focal and non-focal seizures. Comparison results ensure that proposed study is superior to existing methods. Hence, it is expected to perform better at the hospitals for automated classification of focal epileptic seizures in real-time.

A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain

The detection of seizure activity in electroencephalogram (EEG) segments is very important for the classification and localization of epileptic seizures. The evolution of a seizure in an EEG usually appears as a train of non-uniformly spaced spikes and/or as piecewise linear frequency modulated signals. If a seizure is present, then the energy of the EEG is concentrated along the time axis and the frequency axis in the time–frequency plane. However, in the absence of a seizure, the energy of the EEG signal is uniformly distributed along all directions in the time–frequency plane. Based on this observation, we propose a new approach for the detection of a seizure. In this paper, we develop a new feature that exploits the direction of the energy of the signal in the time–frequency domain to distinguish between seizures and non-seizures in an EEG. Our experimental results indicate the superiority of the proposed approach over other conventional time–frequency approaches; for example, the proposed feature set achieves a classification accuracy of 98.25% by only using five features.

Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals.

The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.

S10-4. Role of the medical doctors in longterm VIDO-EEG monitoring

Long-term video-electroencephalography monitoring (LTM) in epilepsy monitoring units (EMUs) is an essential and most meaningful investigation for diagnosis of epilepsy, classification of epileptic seizures and pre-surgical evaluation of patients with intractable localization-related epilepsy. The tapering of antiepileptic drugs is commonly used to record seizures efficiently. However, this practice may expose patients to serious adverse events, such as falls, status epilepticus, psychiatric complication, cardiac events and pulmonary complications. The issue of patients’ safety during LTM is one of the most important tasks. Before the starting of the LTM, the patients’ information (seizure type and frequency, history of seizure clusters, status epilepticus, and postictalpsychosis, number, dosage, and type of AEDs) must be shared in multidisciplinary conference, Based on these information, the AED tapering and safety plan should be developed for each patient. Furthermore, staff education improve patients’ supervision by nurses and EEG technicians, and immediate review of adverse events. The information and knowledge sharing by all the EMU staff might be most important for the patients‘ safety and efficient seizure recording.

Tsallis entropy: as a new single feature with the least computation time for classification of epileptic seizures

Epilepsy is a neurological disease condition in which one experience seizures. These can be recorded using Electroencephalogram which is by nature long term recordings. A lot of work has been done towards automatic detection of these seizures in the literature with both short term and long term recordings. In this work, we have used the standard database of University of Bonn for a four different two class classification problem as addressed by various others in the literature. We have used novel single feature namely Tsallis entropy along with five different classifiers. Comparing with other literatures, we find that our method has the least computation time as low as 0.9 ms. We achieved a highest accuracy of 92.67–100% with Decision tree classifier for the four types of two class classification problem considered. Our method being very simple and also has fastest computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures.

The management of epilepsy in children and adults.

The International League Against Epilepsy has recently published a new classification of epileptic seizures and epilepsies to reflect the major scientific advances in our understanding of the epilepsies since the last formal classification 28 years ago. The classification emphasises the importance of aetiology, which allows the optimisation of management. Antiepileptic drugs (AEDs) are the main approach to epilepsy treatment and achieve seizure freedom in about two-thirds of patients. More than 15 second generation AEDs have been introduced since the 1990s, expanding opportunities to tailor treatment for each patient. However, they have not substantially altered the overall seizure-free outcomes. Epilepsy surgery is the most effective treatment for drug-resistant focal epilepsy and should be considered as soon as appropriate trials of two AEDs have failed. The success of epilepsy surgery is influenced by different factors, including epilepsy syndrome, presence and type of epileptogenic lesion, and duration of post-operative follow-up. For patients who are not eligible for epilepsy surgery or for whom surgery has failed, trials of alternative AEDs or other non-pharmacological therapies, such as the ketogenic diet and neurostimulation, may improve seizure control. Ongoing research into novel antiepileptic agents, improved techniques to optimise epilepsy surgery, and other non-pharmacological therapies fuel hope to reduce the proportion of individuals with uncontrolled seizures. With the plethora of gene discoveries in the epilepsies, "precision therapies" specifically targeting the molecular underpinnings are beginning to emerge and hold great promise for future therapeutic approaches.

Efficient Prediction and Classification of Epileptic Seizures Using EEG Data Based on Univariate Linear Features

Efficient Prediction and Classification of Epileptic Seizures Using EEG Data Based on Univariate Linear Features

Adaptive Hybrid Feature Selection-Based Classifier Ensemble for Epileptic Seizure Classification

Feature selection and ensemble learning can be used to improve the accuracy and robustness of epileptic seizure detection and classification. Unfortunately, a few studies have fully utilized feature selection and ensemble learning. In this paper, we present an adaptive hybrid feature selection-based classifier ensemble (AHFSE) for epileptic seizure classification. The AHFSE creates new sample subsets in every bootstrap using adaptive hybrid feature selection. It combines them using rank aggregation to obtain a distinguished subset of features. These new samples’ subsets are then fed into a classifier. Finally, majority voting is used to complete the detection and classification tasks. The AHFSE is designed to obtain an optimized subset of features based on the different samples in every bootstrap, which have a tendency to generate different results with respect to rank aggregation. With discrete wavelet transform, the experiments based on binary and multi-class tasks show that the AHFSE performs well on the Bonn data set and improves the specificity, sensitivity, or accuracy of the selected features by combining the subsets of different feature selections to obtain new samples within the bagging process. Furthermore, the adaptive process helps the framework obtain the optimum combination of the feature selection algorithm. The AHFSE also obtains more desirable final results in several perspectives, such as: 1) compared with other feature selection methods; 2) compared with other ensemble methods; and 3) compared with other research that uses discrete wavelet transform as a preprocessing step.

Neurotrauma as a factor of epileptogenesis

The presented article deals with traumatic brain injury, which can cause convulsive seizures with the subsequent development of epilepsy, which is especially characteristic of young people. The problems of classification of posttraumatic epileptic seizures, development of their mechanisms, characteristics of diagnosis, as well as the stages of epileptogenesis leading to the development of traumatic epilepsy are considered. The variants of therapy are given in view of traumatic brain injury. It is noted that the use of electrical stimulation during individual phases of paroxysmal oscillations can be an effective method in preventing the development of homeostatic plasticity that is involved in the development of epileptogenesis.

Consciousness or awareness in the classification of focal seizures: Pros and cons

Background: Classification of focal epileptic seizures has used the presence or absence of consciousness for many years. A recent new classification proposed by the ILAE in 2017 uses the term awareness instead of consciousness as a main classifier. Despite this tradition, it has repeatedly been questioned as to whether preservation or impairment of consciousness/awareness contribute to the understanding of the epilepsy syndrome, have a localizing value, and have sufficient interrater reliability when assessing video-recordings of seizures.

Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p < 0.05) and z-score (-1.96 > z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey's range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.

P374 Can diffusion weighted MRI localise abnormalities acutely post-seizure?

Objectives We aimed to evaluate the electroencephalography (EEG) and diffusion weighted magnetic resonance (DWMR) findings obtained after a seizure in the acute period and to find out whether DWMR had any contribution to determine lateralization of epileptogenic activity. Methods Consecutive 43 patients with a seizure admitted to emergency department between July 2016 and February 2017 were evaluated, retrospectively. Patients whose seizures occurred in the last three days were included in the study. Demographic and clinical features, EEG and radiologic findings of the patients were evaluated. Results Mean age of total 43 patients was 48.12 ± 29.7 (18–80) years and 26 (60.5%) of them were female, 17 (39.5%) were male. Patients were divided into 3 groups regarding the classification of epileptic seizures; generalized (n = 17), partial (n = 23) and unclassified seizures (n = 3). Ten patients (23.3%) had the diagnosis of epilepsy previously. Abnormalities in EEG and DWMR were detected in 55.8% and 16.3% of the patients, respectively. In addition, 6 patients of the 7 with MR abnormalities were from partial seizures and 1 patient from generalised seizures groups. Areas of diffusion restriction were consistent with the localization of EEG abnormalities in 4 out of 7 patients. Discussion EEG is a valuable tool in supporting the diagnosis of epilepsy and classification of seizures. DWMR may be used in epilepsy patients to detect the areas that could be the cause of the epileptogenic activity in the post-ictal acute phase. Conclusions Further studies with higher number of patients may investigate the relationship between EEG and DWMR abnormalities. Significance This study may draw attention to relationship between neurophysiological and radiological findings in the acute period after a seizure.

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

Epileptic Seizure Classification of EEGs Using Time-Frequency Analysis Based Multiscale Radial Basis Functions.

The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.

Epilepsi bangkitan umum tonik-klonik di UGD RSUP Sanglah Denpasar-Bali

Introduction: Epilepsy is a neurological disease that is characterized by episodes of seizures which can be accompanied by loss of consciousness of the patient. Although it is usually accompanied by loss of consciousness, there are several types of seizures without loss of consciousness. The disease is caused by the instability of electrical charges in the brain which in turn interfere with muscle coordination and manifest in muscle stiffness or repetitive pounding on the muscle. Classification of epileptic seizure according to ILAE 1981 include some general seizures, partial / focal seizures, and not classified seizures. Case: A 57-year-old woman, Balinese, come to the ER with complaints a limp, escorted by residents because of fainting. Patients do not know what happened so he brought the ER. Patients know who she is, what's her name, where she was, and the current time. Patient say she felt a little dizziness, no nausea, no vomiting. Patients are found to fall unconscious. At that time the patient was at work with his daily activity carrying stuff. Before fainting said patient lying at the scene while stamping hands and feet. Not checked how long the patient was lying. From families were dropping, the patient has a history of epilepsy, had been hospitalized in the hospital with a diagnosis of epilepsy, but never control the drug after the return of hospitalization.

Performance of epileptic single-channel scalp EEG classifications using single wavelet-based features.

Classification of epileptic scalp EEGs are certainly ones of the most crucial tasks in diagnosis of epilepsy. Rather than using multiple quantitative features, a single quantitative feature of single-channel scalp EEG is applied for classifying its corresponding state of the brain, i.e., during seizure activity or non-seizure period. The quantitative features proposed are wavelet-based features obtained from the logarithm of variance of detail and approximation coefficients of single-channel scalp EEG signals. The performance on patient-dependent based epileptic seizure classifications using single wavelet-based features are examined on scalp EEG data of 12 children subjects containing 79 seizures. The 4-fold cross validation is applied to evaluate the performance on patient-dependent based epileptic seizure classifications using single wavelet-based features. From the computational results, it is shown that the wavelet-based features can provide an outstanding performance on patient-dependent based epileptic seizure classification. The average accuracy, sensitivity, and specificity of patient-dependent based epileptic seizure classification are, respectively, 93.24%, 83.34%, and 93.53%.

Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.

In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event. Overall, the excellent performance on patient-dependent epileptic seizure classification is obtained with the average accuracy, sensitivity, and specificity of, respectively, 0.9687, 0.7299, and 0.9813. The vector composed of two wavelet-based features of scalp EEGs provide the best performance on patient-dependent epileptic seizure classification in most cases, i.e., 19 cases out of 24. The wavelet-based features corresponding to the 32-64, 8-16, and 4-8 Hz subbands of scalp EEGs are the mostly used features providing the best performance on patient-dependent classification. Furthermore, the performance on both patient-dependent and patient-independent epileptic seizure classifications are also validated using tenfold cross-validation. From the patient-independent epileptic seizure classification validated using tenfold cross-validation, it is shown that the best classification performance is achieved using the wavelet-based features corresponding to the 64-128 and 4-8 Hz subbands of scalp EEGs.

Time-frequency image based features for classification of epileptic seizures from EEG signals

In this paper, a new method for the automatic classification of seizures based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of seizures is a crucial part of the diagnosis and treatment of epileptic seizures. TFI has been obtained by the smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal. The contrast stretching based pre-processing is used to increase the dynamic range of image pixels of TFI. The TFI has been segmented into rhythms of EEG signals based on frequency-bands. The features extracted from segmented images have been used as input, set to least squares support vector machines (LS-SVM) classifier together with the linear kernel, radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of seizure from EEG signals. The proposed method for classification of EEG signals has provided better classification accuracy than other existing methods. The experimental results are presented to show the effectiveness of the proposed method for the classification of seizure from EEG signals.

Classification of epileptic seizures and epilepsy (2017)

Classification of epileptic seizures and epilepsy (2017)