Automatic Seizure Detection Research Articles

Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure.

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder-Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network's depth.

Automated Seizure Detection in Patients with Cardiac Arrest: A Retrospective Review of Ceribell™ Rapid-EEG Recordings.

In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest. This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity. In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus. The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.

EpilepIndex: A novel feature engineering tool to detect epilepsy using EEG signals.

Epilepsy is a common neurological disease characterized by seizures. A person with a seizure onset can lose consciousness which in turn can lead to fatal accidents. Electroencephalogram (EEG) is a recording of the electrical signals from the brain which is used to analyse the epileptic seizures. Physical visual examination of the EEG by trained neurologists is subjective and highly difficult due to the non-linear complex nature of the EEG. This opens a window for automatic detection of epileptic seizures using machine learning methods. In this work, we have used a standard database that consists of five different sets of EEG data including the epileptic EEG. Using this data, we have devised a novel 22 possible clinically significant cases with the combination of binary and multi class type of classification problem to automatically classify epileptic EEG. As the EEG is non-linear, we have devised 11 statistically significant non-linear entropy features to extract from this database. These features are fed to 10 different classifiers of various types for each of the 22 clinically significant cases and their classification accuracy is reported for 10-fold cross validation. Random Forest and Optimized Forest classifiers reported accuracies above 90% for all 22 cases considered in this study. Such vast possible clinically significant 22 cases from the combination of the data from the database considered has not been in the literature with the best of the knowledge of the authors. Comparing with the literature, several studies have presented one or few combinations of these 22 cases in this work. In comparison to similar works, the accuracies obtained by the classifiers were highly competitive. In addition, a novel integrated epilepsy detection index named EpilepIndex (IED) is able to differentiate between epileptic EEG and a normal EEG with 100% accuracy.

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM.

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals.

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique's accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.

Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.

Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet.

Automatic seizure detection technology has important implications for reducing the workload of neurologists for epilepsy diagnosis and treatment. Due to the unpredictable nature of seizures, the imbalanced classification of seizure and nonseizure data continues to be challenging. In this work, we first propose a novel algorithm named the borderline nearest neighbor synthetic minority oversampling technique (BNNSMOTE) to address the imbalanced classification problem and improve seizure detection performance. The algorithm uses the nearest neighbor notion to generate nonseizure samples near the boundary, then determines the seizure samples that are difficult to learn at the boundary, and lastly selects seizure samples at random to be used in the synthesis of new samples. In view of the characteristic that electroencephalogram (EEG) signals are one-dimensional signals, we then develop a 1D-MobileNet model to validate the algorithm's performance. Results demonstrate that the proposed algorithm outperforms previous seizure detection methods on the CHB-MIT dataset, achieving an average accuracy of 99.40%, a recall value of 87.46%, a precision of 97.17%, and an F1-score of 91.90%, respectively. We also had considerable success when we used additional datasets for verification at the same time. Our algorithm's data augmentation effects are more pronounced and perform better at seizure detection than the existing imbalanced techniques. Besides, the model's parameters and calculation volume have been significantly reduced, making it more suitable for mobile terminals and embedded devices.

Interactive local and global feature coupling for EEG-based epileptic seizure detection

Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate the progress of epilepsy diagnosis. Current seizure detection methods based on deep learning usually rely on single convolutional neural network (CNN) or recurrent neural network (RNN) models. In terms of feature extraction, single model often has limitations. Since CNN is good at extracting local features, and Transformer can capture global information, we put forward a seizure detection method based on interactive local and global feature coupling. Local feature and global representation of the EEG are respectively extracted by convolution operation and self-attention mechanism. To make up of the shortcomings of local feature and global representation, a feature coupling block (FCB) is utilized to fuse the two kinds of information in an interactive way. The enhanced feature representation is fed to the classifier for seizure and normal EEG classification. Extensive experiments are conducted on CHB-MIT and Siena scalp EEG datasets. Experimental results demonstrate that the method can effectively perform epileptic seizure detection from the original EEG signals without extra feature extraction.

Automatic seizure detection by convolutional neural networks with computational complexity analysis

Background and ObjectivesNowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. MethodsThe methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. ResultsDepending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. ConclusionsThrough the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

An effectual IOT coupled EEG analysing model for continuous patient monitoring

A persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidimensional clinical data. Such data processing and analysis present a variety of computing research difficulties. One of the most often used signals obtained from the human brain in both science and therapeutic settings is the electroencephalogram (EEG). It is an effective resource for offering insightful knowledge of the mechanics of the brain. EEG signal analyses that are precise and thorough are crucial in the diagnosis of brain diseases like epilepsy. The EEG signal is well-liked in the field of biomedical study because of its notable temporal solution, non-invasive recording setup, and convenience with minimal costs. Currently, skilled neurologists make a diagnosis by visually examining EEG information to determine the most likely course of action. It takes time and is error-prone to visually inspect high-dimensional and non-stationary EEG recordings. An automated seizure detector is a crucial tool for better understanding how seizures are generated as well as a relief for medical experts who become exhausted when viewing a continuous, massive, long-term recording. As it records from at least 16 channels, the multichannel automatic seizure detection system is large. It is difficult to use such a method on distantly located subjects. The suggested system gathers data from IoT devices, and patient history-related electronic clinical data that are saved in the cloud were subjected to predictive analytics. The Bi-LSTM-based intelligent healthcare system for tracking and precisely forecasting brain risk. The trade-off between the current system and the current methods is anticipated in the results section.

Application of Machine Learning in Epileptic Seizure Detection.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

A deep learning approach for epilepsy seizure detection using EEG signals

Electroencephalogram (EEG) is an effective non-invasive way to detect sudden changes in neural brain activity, which generally occurs due to excessive electric discharge in the brain cells. EEG signals could be helpful in imminent seizure prediction if the machine could detect changes in EEG patterns. In this study, we have proposed a one-dimensional Convolutional Neural network (CNN) for the automatic detection of epilepsy seizures. The automated process might be convenient in the situations where a neurologist is unavailable and also help the neurologists in proper analysis of EEG signals and case diagnosis. We have used two publicly available EEG datasets, which were collected from the two African countries, Guinea-Bissau and Nigeria. The datasets contain EEG signals of 318 subjects. We have trained and verify the performance of our model by testing it on both the datasets and obtained the highest accuracy of 82.818%.

Continuous Seizure Detection Based on Transformer and Long-Term iEEG.

Automatic seizure detection algorithms are necessary for patients with refractory epilepsy. Many excellent algorithms have achieved good results in seizure detection. Still, most of them are based on discontinuous intracranial electroencephalogram (iEEG) and ignore the impact of different channels on detection. This study aimed to evaluate the proposed algorithm using continuous, long-term iEEG to show its applicability in clinical routine. In this study, we introduced the ability of the transformer network to calculate the attention between the channels of input signals into seizure detection. We proposed an end-to-end model that included convolution and transformer layers. The model did not need feature engineering or format transformation of the original multi-channel time series. Through evaluation on two datasets, we demonstrated experimentally that the transformer layer could improve the performance of the seizure detection algorithm. For the SWEC-ETHZ iEEG dataset, we achieved 97.5% event-based sensitivity, 0.06/h FDR, and 13.7 s latency. For the TJU-HH iEEG dataset, we achieved 98.1% event-based sensitivity, 0.22/h FDR, and 9.9 s latency. In addition, statistics showed that the model allocated more attention to the channels close to the seizure onset zone within 20 s after the seizure onset, which improved the explainability of the model. This paper provides a new method to improve the performance and explainability of automatic seizure detection.

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals.

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

Seizure detection algorithm based on fusion of spatio-temporal network constructed with dispersion index

Epilepsy is one of the common brain disorders. Traditional seizure detection is done by electroencephalography (EEG) technicians, which takes a lot of time. Therefore, this paper proposes a new method for automatic seizure detection. In this paper, we use multivariate variational mode decomposition (MVMD) method for modal decomposition of multi-channel EEG signals and introduce dispersion index (DI) as a new brain network weight calculation method which is based on dispersion entropy (DE) to extract the features of single-channel temporal brain network and multi-channel spatial brain network for seizure detection. In single-channel temporal brain network, the network is constructed by weighted horizontal visibility graph (WHVG) method which considers the sample points as network nodes. In multi-channel spatial brain networks, DI is used to calculate the correlation between channels and select the threshold to construct the ternary-valued network of channels. We perform the validation on two publicly available datasets. The results show that the classification results for F1, AUC, ACC, PRE, SEN and SPE on CHB-MIT dataset are 97.89%, 97.81%, 97.83%, 97.56%, 98.24% and 97.39%, respectively. In addition, the results for F1, AUC, ACC, PRE, SEN and SPE on Siena scalp dataset are 99.21%, 99.19%, 99.19%, 99.28%, 99.14%, and 99.24%, respectively. The method proposed in this paper achieves good results on both datasets. In general, the joint detection of temporal and spatial networks is promising, and DI can serve as a valid indicator of correlation.

Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review

Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment.

Explainable automated seizure detection using attentive deep multi-view networks

Manual inspection of Electroencephalography (EEG) signals to detect epileptic seizures is time-consuming and prone to inter-rater variability. Moreover, EEG signals are contaminated with different noise sources, e.g., patient movement during seizures, making the accurate identification of seizure activities challenging. In a Multi-View seizure detection system, since seizures do not uniformly affect the brain, some views likely play a more significant role in detecting seizures and should therefore be assigned a higher weight in the concatenation step. To address this dynamic weight assignment issue and also create a more interpretable model, in this work, we propose a fusion attentive deep multi-view network (fAttNet). The fAttNet combines temporal multi-channel EEG signals, wavelet packet decomposition (WPD), and hand-engineered features as three key views. We also propose an artifact rejection approach to remove unwanted signals not originating from the brain. Experimental results on the Temple University Hospital (TUH) seizure database demonstrate that the proposed method has increased performance over the state-of-the-art methods, raising accuracy, and F1-score from 0.82 to 0.86, and 0.78 to 0.81, respectively. More importantly, the proposed method is interpretable for medical professionals, assisting clinicians in identifying the regions of the brain involved in the seizures.

Spatiotemporal analysis of interictal EEG for automated seizure detection and classification

ObjectiveSeizure type classification is important as therapy differs for different epilepsy subtypes.Currently, skilled neurologists classifyseizuresbased on visual analysis. However, manual EEG inspection is time-consuming, laborious,subjective, and prone to misclassification due to artifacts andEEGvariability. This work aims to address these limitations. MethodsIn this work, a quick, robust, and accurate spatiotemporal analytical algorithm is developed to classify epileptic seizures.TheEEG dataset is sampled at 125 Hz using aNicolet EEG system.Robust preprocessing, feature extraction, andoptimal classifiers captured IEDs (Interictal Epileptiform Discharges), includingspikes, sharps, slowwaves, and Spike-Wave Discharges (SWD). ResultsThe developed classifier resultsarevalidated againstclinicalimpressionsprovided by experienced epileptologists. Thealgorithm automatically classifies the EEG datainto four types: normal, focal, generalized, and absence, with 93.18 % accuracy (n = 88). ConclusionTheresultssuggest a novel way toscreen epileptic subjects without false positives (accuracy: 94.32 %, n = 88) and tentatively identify the seizure type. Blind validation further confirms the generalizability of the classifier (accuracy: 90.90 %, n = 11). SignificanceThe developed algorithm reduces the workload of neurologists for epilepsy screening and identifies seizure onset zone, temporal spread, and overall scalp distribution of epileptic activities.

Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG

Epilepsy is a complicated neurological disorder, that features recurrent seizures. During a seizure, the repetitive action potentials lead to a high-frequency burst, and a hyper synchronization happens among the activities of a population of cortical neurons concurrently. In this study, we propose a repetition-based seizure detection method, using Gray Level Co-occurrence Matrix (GLCM) and Electroencephalogram (EEG) signals. In this method, imaged-EEGs are made from the Short Time Fourier Transform (STFT) coefficients of two sequential epochs. The GLCMs of these images are calculated and one feature named diagGLCM is extracted from each co-occurrence matrix. This feature demonstrates the repetition of the STFT coefficient of a frequency band in two sequential epochs, which are used for a classification task between ictal (during a seizure) and inter-ictal (between seizures) time segments. Using one frequency band’s STFT repetition, a patient-specific seizure detection algorithm is developed with an accuracy of 99.56%, a sensitivity of 99.52%, and specificity of 99.62%. This accurate classification shows the efficiency of the extracted feature which can be a biomarker for the ictal repetitive action potentials.