Early Detection Of Epileptic Seizures Research Articles

BIOM-14. INCREASED RATIO OF GLUTAMATE TO TOTAL CREATINE IN GLIOBLASTOMA PATIENTS WITH THE POST-OPERATIVE EPILEPSY

Abstract Epilepsy in glioblastoma patients significantly reduces their quality of life; however, little is known about the association between predicting epilepsy and metabolites in tumors. In this study, we used 3.0-T magnetic resonance imaging (MRI) and 1H-magnetic resonance spectroscopy (MRS) to quantify metabolite concentrations in patients with varying epilepsy histories. MRS data were quantified with linear combination model (LC-Model) software. From January 2013 to April 2020, fifty-one patients with primary glioblastoma underwent pretreatment 3.0-T MRI/1H-MRS scanning. Twenty-one patients (41%) suffered from epilepsy during the postoperative course, although forty-seven patients (97%) took anti-epileptic drug after surgery. MRS data were compared between groups with and without the post-operative epilepsy. The higher ratios of glutamate (Glu) and glutamate + glutamine (Glx) to total creatine (tCr) in the tumor were significantly associated with epilepsy history (Glu/tCr P < 0.001, Glx/tCr P = 0.004, respectively). The receiver operating characteristic curve analysis showed that a Glu/tCr value of 1.81 was 70% sensitive and 90% specific for the prediction of epilepsy during the postoperative course (area under curve: 0.82). These results suggest that the Glu/tCr ratio in tumors can be a predictive maker for early detection of epileptic seizures after surgery in glioblastoma patients.

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means.

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Epilepsy is the most common neurological disease defined as a central nervous system disorder that is characterized by recurrent seizures. While electroencephalography (EEG) is an essential tool for monitoring epilepsy patients' brain activity and diagnosing epilepsy, Visual detection of the EEG signal to identify epileptic seizures is a time-consuming approach that might result in human error. Therefore, an early and precise epilepsy diagnosis is critical to reducing the risk of future seizures. This paper aims to increase epileptic seizure detection accuracy in a balanced dataset while reducing the execution time. To address this, we proposed a hybrid system of supervised and unsupervised machine learning algorithms to construct a computationally efficient and scalable model for the early detection of epileptic seizures from two-class EEG datasets. First, Discrete Wavelet Transform (DWT) was applied to the EEG signal to decompose it into frequency sub-bands. Then these EEG extracted features were fed into the Gaussian Mixture Model (GMM) for partitioning these features into two clusters: epilepsy or not. Lastly, the clusters' output was evaluated with the random forest classifier. In addition, Principal Component Analysis (PCA) was used to reduce the EEG features and to reduce further the features obtained after conducting DWT on the EEG signal to determine the impacts of dimension reduction on this system performance. The experimental results show that the highest accuracy was achieved by the hybrid system of GMM with random forest with DWT features with an accuracy of 93.62 %.

A systematic review on detection and estimation algorithms of EEG signal for epilepsy

Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.

A systematic review on detection and estimation algorithms of EEG signal for epilepsy

Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.

Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns

Early detection of epileptic seizures has a significant impact on patient outcomes. A novel pipeline for EEG-based epileptic seizure detection is here presented in which frequency factorisation is carried out on EEG signals by using constrained Singular Spectrum Analysis (SSA), coupled with one dimensional Local Binary Patterns (1-D LBP). The resulting frequency pattern transformation is classified via a Support Vector Machine (SVM) using Half Total Error Rate (HTER) in order to evaluate the performance of the proposed pipeline in a class-imbalanced context, with results compared against 1-D LBP on reference datasets. The results are tested against other comparable pipelines and demonstrate best-in-class performance.

IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

Epilepsy, a fatal neurological disorder, has been emerged as a worldwide problem and is one of the major risks to human lives. There exists an urgent need for an efficient technique for early detection of epileptic seizures at its initial stage in order to save the lives of thousands of epileptic patients annually. Now a days, internet of things in combination with machine learning techniques and cloud computing services has emerged as a powerful technology to resolve many problems in healthcare sector. This paper also presents an automatic epileptic seizure detection system and its layered architecture for early detection of epileptic seizures using existing communication technologies in collaboration with machine learning and cloud computing. This model transmits sensed EEG signals from patient’s scalp to cloud through 4G cellular network or Wi-Fi network. At cloud, EEG signals are processed using Fast Walsh Hadamard transform and higher order spectra (HOS) based feature extraction for extracting higher order statistics and entropy-based features. The correlation-based feature selection algorithm has been employed for reducing the dimensionality of EEG datasets so as to tackle the problem of large volume of data and to reduce delays in service offered to the end user. Random Forest algorithm has been employed for classification of EEG signals into three different seizure stages, viz., normal, preictal and ictal. For performance analysis, other well-known machine learning algorithms like Bayes Net, Naive Bayes, Multilayer Perceptron, Radial Basis function neural network and C4.5 Decision Tree are also considered. The simulation and testing results show that Random Forest classifier provides maximum values of classification accuracy of 99.40%, sensitivity of 99.40% and specificity of 99.66%, minimum mean square error of 0.0871 along with optimum training time of 20 ms, which makes this model more real time compatible, thereby making HOS features based Random Forest algorithm’s cloud model an efficient technique for early and automatic detection of epileptic seizures in real time.

Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM Architecture

Epilepsy is the most unpredictable and recurrent disease among neurological diseases. Early detection of epileptic seizures can play a critical role in providing timely treatment to patients especially when a patient is in a remote area. This paper uses deep learning framework to detect epilepsy in the Electroencephalography (EEG) signal. The dataset used is publicly available and has a recording of three kinds of EEG signals: pre-ictal, inter-ictal (seizure-free epileptic) and ictal (epileptic with seizure). The proposed Long Short-Term Memory (LSTM) classifier classifies these three kinds of signals with up to 95% accuracy. For binary classification such as detection of inter-ictal or ictal only, its accuracy increases to 98%. The EEG signal is modelled as wide sense non-stationary random signal. Hurst Exponent and Auto-regressive Moving Average (ARMA) features are extracted from each signal. In this work, two different configurations of LSTM architecture: single-layered memory units and double-layered memory units are also modelled. After standardising the features, double-layered LSTM approach gives the highest accuracy in comparison to previously used Support Vector Machine (SVM) classifier and proved to be computationally efficient at Graphics Processing Unit (GPU).

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics.

Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations

Epilepsy is one of the most common central nervous system disorders. Epileptic people suffer from recurrent seizures depending on many trigger factors such as genetic, physiologic, brain damage, etc. Epileptic seizures occur at unpredictable times and mostly without warning. During seizures, epileptic people have distraction or involuntary spasms that might even result in serious physical injuries or death. Therefore, the detection of epilepsy still challenges the neurologists in terms of prediction of seizure times and classifying the brain signals received from different zones of brain. This study presents an efficient procedure that provides an accurate classification of Electroencephalogram (EEG) signals for early detection of epileptic seizures. Essentially, this procedure hybridizes many tools such as artificial neural networks (ANNs), gradient based algorithms, genetic algorithms (GAs), feature extraction with discrete wavelet transforms (DWT) and fuzzy relations for reducing dimensionality of features. In analysis, ANNs are trained by the gradient based algorithms and GAs considering early stopping, cross-validation and information criteria. In order to ensure an accurate classification performance, the automated multi-resolution signal processing technique splits EEG signals into the detailed partitions with different bandwidths, and then decomposes them into detail and approximation coefficients by means of DWT at the different decomposition levels. Thus, some specific latent features that characterize the nonlinear and dynamical structures of EEG signals are acquired from these coefficients. The fuzzy relations bring out the significant components by reducing the dimension of feature matrix. To detect the epileptic behaviors in EEG signals, these selected components are processed by ANNs based cross-entropy and information criteria. According to analysis results, this approach not only allows making deeply analysis of EEG signals for detection of epilepsy, but also provides the best model configurations for ANNs in terms of reliability and complexity.

A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures.

In this paper we describe a simple and very fast method of data acquisition, feature extraction and feature space creation for epileptic seizure detection. The scalp electroencephalogram (EEG) dataset [1, 2] collected at the Children's Hospital Boston from 22 pediatric patients having 192 intractable seizures (available as CHB-MIT database) is used to assess this simple approach against existing ones [1, 3], with very positive results reaching up to 99.48% Sensitivity.

Early detection of epilepsy seizures based on a weightless neural network.

This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.

Multi-task seizure detection: addressing intra-patient variation in seizure morphologies

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intra-patient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB–MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.

Early Detection of Epileptic Seizures in Sparse Domains

Early Detection of Epileptic Seizures in Sparse Domains

Early detection of epileptic seizures based on parameter identification of neural mass model

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0%±10.0% and false positive rate was 0.20±0.22 per hour).

Review: A Survey of Performance and Techniques for Automatic Epilepsy Detection

Fil: Orosco, Lorena Liliana. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina