Journal articles on the topic 'Epileptic seizures detection'
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Sharmila, Ashok, and Purusothaman Geethanjali. "A review on the pattern detection methods for epilepsy seizure detection from EEG signals." Biomedical Engineering / Biomedizinische Technik 64, no. 5 (September 25, 2019): 507–17. http://dx.doi.org/10.1515/bmt-2017-0233.
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Abstract Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians’ encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.
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Shoeibi, Afshin, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, et al. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review." International Journal of Environmental Research and Public Health 18, no. 11 (May 27, 2021): 5780. http://dx.doi.org/10.3390/ijerph18115780.
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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
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Hashem Attia, Atef, and Ashraf Mahroos Said. "Brain seizures detection using machine learning classifiers based on electroencephalography signals: a comparative study." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 803. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp803-810.
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The <span>paper demonstrates various machine learning classifiers, they have been used for detecting epileptic seizures quickly and accurately through electroencephalography (EEG), in real time. Symptoms of epilepsy are caused abnormal brain activity. Analyzing and detecting epileptic seizures presents many challenges because EEG signals are non-stationary, and the patterns of the seizure vary for each patient. Moreover, the EEG signals are noisy, and this affect the process of seizure detection. On the other hand, Machine learning algorithms are very accurate, adaptive and generalize very well when provided with diverse and big training data and can easily analyze complex structure of the EEG signal despite the noisiness when compared to other methods. With this approach the features of epileptic seizures can be learned and used to correctly identify other seizure cases. The demonstration states a comparison between various classifiers, including random forests, K-nearest neighbors (K-NN), decision trees, support vector machine (SVM), logistic regression and naïve bayes. Different performance metrics is used such as accuracy, receiver operating characteristics (ROC), mean absolute error (MAE), root-mean-square error (RMSE) and most importantly detection time for each algorithm. The Bonn university dataset has been used for demonstration process for the classification of the epileptic seizure.</span>
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Dhar, Puja, Vijay Kumar Garg, and Mohammad Anisur Rahman. "Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals." Journal of Healthcare Engineering 2022 (March 16, 2022): 1–14. http://dx.doi.org/10.1155/2022/3491828.
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One of the most common neurological disorders is epilepsy, which disturbs the nerve cell activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to detect epilepsy and are considered standard techniques to diagnose epilepsy conditions. EEG monitors and records the brain activity of epilepsy patients, and these recordings are used in the diagnosis of epilepsy. However, extracting the information from the EEG recordings manually for detecting epileptic seizures is a difficult cumbersome, error-prone, and labor-intensive task. These negative attributes of the manual process increase the demand for implementing an automated model for the seizure detection process, which can classify seizure and nonseizures from EEG signals to help in the timely identification of epilepsy. Recently, deep learning (DL) and machine learning (ML) techniques have been used in the automatic detection of epileptic seizures because of their superior classification abilities. ML and DL algorithms can accurately classify different seizure conditions from large-scale EEG data and provide appropriate results for neurologists. This work presents a feature extraction-based convolutional neural network (CNN) to sense and classify different types of epileptic seizures from EEG signals. Different features are analyzed to classify seizures via EEG signals. Simulation analysis was managed to investigate the classification performance of the hybrid CNN-RNN model in terms of different achievement metrics such as accuracy, precision, recall, f1 score, and false-positive rate. The results validate the efficacy of the CNN-RNN model for seizure detection.
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Saranya, D., and A. Bharathi. "Automatic detection of epileptic seizure using machine learning-based IANFIS-LightGBM system." Journal of Intelligent & Fuzzy Systems 46, no. 1 (January 10, 2024): 2463–82. http://dx.doi.org/10.3233/jifs-233430.
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A sudden increase in electrical activity in the brain is a defining feature of one of the severe neurological diseases known as epilepsy. This abnormality appears as a seizure, and identifying seizures is an important field of research. An essential technique for examining the features of neurological issues brain activities, and epileptic seizures is electroencephalography (EEG). In EEG data, analyzing epileptic irregularities visually requires a lot of time from neurologists. For accurate detection of epileptic seizures, numerous scientific techniques have been used with EEG data, and most of these techniques have produced promising results. For EEG signal classification with a high classification accuracy rate, the present research proposes an enhanced machine learning-based epileptic seizure detection model. The present research provides a hybrid Improved Adaptive Neuro-Fuzzy Inference System (IANFIS)-Light Gradient Boosting Machine (LightGBM) technique for automatically detecting and diagnosing epilepsy from EEG data. The experimental findings were supported by EEG records made available by the German University of Bonn and scalp EEG data acquired at Children’s Hospital Boston. The suggested IANFIS-LightGBM, according to the results, offers the most significant classification accuracy ratings in both situations.
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Prasanna, J., M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damaševičius, Nanjappan Jothiraj Sairamya, and S. Thomas George. "Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey." Journal of Personalized Medicine 11, no. 10 (October 15, 2021): 1028. http://dx.doi.org/10.3390/jpm11101028.
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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
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Mansouri, Amirsalar, Sanjay P. Singh, and Khalid Sayood. "Online EEG Seizure Detection and Localization." Algorithms 12, no. 9 (August 23, 2019): 176. http://dx.doi.org/10.3390/a12090176.
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Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists—a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.
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Cogan, Diana, Javad Birjandtalab, Mehrdad Nourani, Jay Harvey, and Venkatesh Nagaraddi. "Multi-Biosignal Analysis for Epileptic Seizure Monitoring." International Journal of Neural Systems 27, no. 01 (November 8, 2016): 1650031. http://dx.doi.org/10.1142/s0129065716500313.
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Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient’s physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.
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Vijay Kakade, Meenal, Chandrakant J. Gaikwad, and Vijay R. Dahake. "Epileptic Seizure Detection Using Artifact Reduction and HOS Features of WPD." ITM Web of Conferences 32 (2020): 02008. http://dx.doi.org/10.1051/itmconf/20203202008.
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The use of computer aided diagnosis systems for disease identifiscation, based on signal processing, image processing and video processing terminologies is common due to emerging technologies in medical field. The detection of epilepsy seizures using EEG recordings is done by different signal processing techniques. To reduce the disability caused by the uncertainty of the occurrence of seizures, a recording system which shall result accurate and early detection of seizure with quick warning is greatly desired. To optimize the performance of EEG based epilepsy seizures detection, in this work we are presenting a method based on two key algorithms. Here, we propose unique algorithm based on SWT (Stationary Wavelet Transform), for easier seizure analysis process, along with improved performance of the application of seizure detection algorithms. Then, we propose the algorithm for feature extraction that makes use of Higher Order Statistics of the coefficients that are calculated using Wavelet Packet Decomposition (WPD).This helps in improving the epilepsy seizures detection performance. The proposed methods helps to improve the overall efficiency and robustness of EEG based epilepsy seizures detection system.
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Et. al., Nazia Parveen,. "Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2533–39. http://dx.doi.org/10.17762/turcomat.v12i2.2202.
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In this paper, the authors propose a new technique for the classification of seizures, non-seizures, and seizure-free EEG signals based on non-linear trajectories of EEG signals. The EEG signals are decomposed using the EMD technique to obtain intrinsic mode functions (IMFs). The phase space of these IMFs is then reconstructed using a novel technique of higher-order dimensions (3D, 4D, 5D, 6D, 7D, 8D, 9D, and 10D). The existing techniques of seizure detection have deployed 2D & 3D phase–space reconstruction only. The Euclidean distance of all higher-order PSR is used as a feature to classify seizures, non-seizures, and seizure-free EEG signals. The performance of the proposed method is analyzed on the Bonn University database in which 7D reconstructed phase space classification accuracy of 99.9% has been achieved both using Random Forest classifier and J48 decision tree.
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Hussen, Hoger Mahmud. "The State of the Art in Feature Extraction Methods for EEG Classification." UHD Journal of Science and Technology 3, no. 2 (July 25, 2019): 16. http://dx.doi.org/10.21928/uhdjst.v3n2y2019.pp16-23.
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Epileptic seizure is a neurological disease that is common around the world and there are many types (e.g. Focal aware seizures and atonic seizure) that are caused by synchronous or abnormal neuronal activity in the brain. A number of techniques are available to detect the brain activities that lead to Epileptic seizures; one of the most common one is Electroencephalogram (EEG) that uses visual scanning to measure brain activities generated by nerve cells in the cerebral cortex. The techniques make use of different features detected by EEG to decide on the occurrence and type of seizures. In this paper we review EEG features proposed by different researches for the purpose of Epileptic seizure detection, also analyze, and compare the performance of the proposed features.
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Gajic, Dragoljub, Zeljko Djurovic, Stefano Di Gennaro, and Fredrik Gustafsson. "CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION." Biomedical Engineering: Applications, Basis and Communications 26, no. 02 (March 12, 2014): 1450021. http://dx.doi.org/10.4015/s1016237214500215.
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The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.
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Statsenko, Yauhen, Vladimir Babushkin, Tatsiana Talako, Tetiana Kurbatova, Darya Smetanina, Gillian Lylian Simiyu, Tetiana Habuza, et al. "Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach." Biomedicines 11, no. 9 (August 24, 2023): 2370. http://dx.doi.org/10.3390/biomedicines11092370.
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Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
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Hilal, Anwer Mustafa, Amani Abdulrahman Albraikan, Sami Dhahbi, Mohamed K. Nour, Abdullah Mohamed, Abdelwahed Motwakel, Abu Sarwar Zamani, and Mohammed Rizwanullah. "Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder." Biology 11, no. 8 (August 15, 2022): 1220. http://dx.doi.org/10.3390/biology11081220.
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Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.
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Tran, Ly V., Hieu M. Tran, Tuan M. Le, Tri T. M. Huynh, Hung T. Tran, and Son V. T. Dao. "Application of Machine Learning in Epileptic Seizure Detection." Diagnostics 12, no. 11 (November 21, 2022): 2879. http://dx.doi.org/10.3390/diagnostics12112879.
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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.
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Vieira, Jusciaane Chacon, Luiz Affonso Guedes, Mailson Ribeiro Santos, and Ignacio Sanchez-Gendriz. "Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals." Sensors 23, no. 24 (December 16, 2023): 9871. http://dx.doi.org/10.3390/s23249871.
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Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models—without resorting to deep learning—is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.
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Yu, Jianan, Rui Min, Yun Yu, Xiaorui Hu, Xiandong Fu, and Nannan Chi. "Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology." Contrast Media & Molecular Imaging 2022 (August 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/3176134.
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Liver cirrhosis is a clinical chronic developmental liver disease, which is caused by long-term or repeated effects of liver dysfunction, and there are more and more cases of epileptic seizures in patients with liver cirrhosis and HEV infection. This article aims to study how to analyze epileptic seizures in patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology. This article proposes a deep learning neural network algorithm based on deep multimodal fusion technology, and how to use this algorithm to automatically detect and classify epileptic seizures. The data in the experiment in this article show that the prevalence of epilepsy accounts for 1% of the world's population, about 56.7 million people, and 1 in 25 people may have an epileptic seizure at some time in their lives, and in each person’s life, the probability of seizures due to various reasons is 10%. In 2016, the proportion of males with cirrhosis reached 16%, females reached 8%, and males were 8% higher than females, which is a full double. The test results show that with the increase in patients with cirrhosis and overlapping HEV infection, the frequency of epileptic seizures is also getting higher and higher, indicating that the frequency of epileptic seizures has been increased in patients with cirrhosis and overlapping HEV infection. Therefore, it is imperative to analyze the epileptic seizures of patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology.
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Zhang, Jingwei, Christos Chatzichristos, Kaat Vandecasteele, Lauren Swinnen, Victoria Broux, Evy Cleeren, Wim Van Paesschen, and Maarten De Vos. "Automatic annotation correction for wearable EEG based epileptic seizure detection." Journal of Neural Engineering 19, no. 1 (February 1, 2022): 016038. http://dx.doi.org/10.1088/1741-2552/ac54c1.
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Abstract Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms. Approach. This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with (a) original vEEG seizure annotations, (b) visually corrected seizure annotations, and (c) automatically corrected seizure annotations. Main results. The automated seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG. Significance. The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.
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Zhu, Xinzhong, Huiying Xu, Jianmin Zhao, and Jie Tian. "Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity." Complexity 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5674392.
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Epilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.
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BENCHAIB, YASMINE. "IMPROVED ARTIFICIAL NEURAL NETWORK FOR EPILEPTIC SEIZURES DETECTION." Journal of Mechanics in Medicine and Biology 21, no. 06 (July 28, 2021): 2150045. http://dx.doi.org/10.1142/s0219519421500457.
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Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.
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Wu, Jiang, Tengfei Zhou, and Taiyong Li. "Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting." Entropy 22, no. 2 (January 24, 2020): 140. http://dx.doi.org/10.3390/e22020140.
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Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.
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Abiyev, Rahib, Murat Arslan, John Bush Idoko, Boran Sekeroglu, and Ahmet Ilhan. "Identification of Epileptic EEG Signals Using Convolutional Neural Networks." Applied Sciences 10, no. 12 (June 13, 2020): 4089. http://dx.doi.org/10.3390/app10124089.
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Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
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Panigrahi, Millee, Dayal Kumar Behera, and Krishna Chandra Patra. "Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 884. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp884-891.
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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.
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Stumpp, Lars, Hugo Smets, Simone Vespa, Joaquin Cury, Pascal Doguet, Jean Delbeke, Antoine Nonclercq, and Riem El Tahry. "Vagus Nerve Electroneurogram-Based Detection of Acute Pentylenetetrazol Induced Seizures in Rats." International Journal of Neural Systems 31, no. 07 (May 24, 2021): 2150024. http://dx.doi.org/10.1142/s0129065721500246.
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On-demand stimulation improves the efficacy of vagus nerve stimulation (VNS) in refractory epilepsy. The vagus nerve is the main peripheral parasympathetic connection and seizures are known to exhibit autonomic symptoms. Therefore, we hypothesized that seizure detection is possible through vagus nerve electroneurogram (VENG) recording. We developed a metric able to measure abrupt changes in amplitude and frequency of spontaneous vagus nerve action potentials. A classifier was trained using a “leave-one-out” method on a set of 6 seizures and 3 control recordings to utilize the VENG spike feature-based metric for seizure detection. We were able to detect pentylenetetrazol (PTZ) induced acute seizures in 6/6 animals during different stages of the seizure with no false detection. The classifier detected the seizure during an early stage in 3/6 animals and at the onset of tonic clonic stage of the seizure in 3/6 animals. EMG and motion artefacts often accompany epileptic activity. We showed the “epileptic” neural signal to be independent from EMG and motion artefacts. We confirmed the existence of seizure related signals in the VENG recording and proved their applicability for seizure detection. This detection might be a promising tool to improve efficacy of VNS treatment by developing new responsive stimulation systems.
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Glasstetter, Martin, Sebastian Böttcher, Nicolas Zabler, Nino Epitashvili, Matthias Dümpelmann, Mark P. Richardson, and Andreas Schulze-Bonhage. "Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor." Sensors 21, no. 18 (September 8, 2021): 6017. http://dx.doi.org/10.3390/s21186017.
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Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.
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Saxena, Shikha, and Kamal Kant Gupta. "Detection of interictal epileptiform discharge in focal seizures." Nepal Journal of Neuroscience 20, no. 3 (November 10, 2023): 43–52. http://dx.doi.org/10.3126/njn.v20i3.58795.
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Introduction: Many factors underlying basic epileptic conditions determine the characteristics of epileptic seizures and the therapeutic outcome. Visual obvious abnormalities in resting baseline EEG are cardinal but incompletely understood like feature of seizure onset zone in focal epilepsy and interictal epileptiform discharge. Present study is an attempt to diagnose that evidence of epileptic discharge in temporal lobe epilepsy (TLE), would persist during interictal period in absence of abnormalities in baseline EEG, which could increase the impact of automatic analysis of EEG waves for clinical relevance.
Material and Methods: By using 10-20 system, functional connectivity was estimated in the 20 channels of delta, theta, alpha, beta and gamma frequency bands of EEG, from 16 diagnosed focal epileptic seizure patients and 16 age and sex matched controls. To observe the dynamics of the healthy brain, differ from the brain of dynamically focal epileptic patients during interictal period treated with anti-epileptic drugs in the context of resting state during eye close session of EEG. Such differences can be observed by using absolute spectral power from BESS ((Brain Electro Scan Software) of the Axxonet System and statistically measure by applied unpaired student t -test.
Result: The high significance results in slow frequency EEG waves (delta and theta) in power spectral analysis were observed that demonstrates the potential epileptic discharge occurring during interictal period without visible pathological activity for helping in the diagnosis and lateralization of TLE.
Conclusion: The detailed spectral analysis of EEG waves offers novel insight into focal epileptic patients when visually EEG findings were normal. This linear analysis helpful in extracting information from EEG signals in diagnosing specific neuronal correlates for TLE. Present findings concluded that epileptic discharges occur at different topographical regions of brain during interictal period and power spectral analysis plays a new insight in diagnosis of focal discharge.
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Maksimenko, V. A., A. A. Harchenko, and A. Lüttjohann. "Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity." Information and Control Systems, no. 4 (September 23, 2018): 115–22. http://dx.doi.org/10.31799/1684-8853-2018-4-115-122.
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Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans.
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Rachappa, Chetana, Mahantesh Kapanaiah, and Vidhyashree Nagaraju. "Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (October 7, 2022): 1502. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1502-1509.
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An automated method for accurate prediction of seizures is critical to enhance the quality of epileptic patients While numerous existing studies develop models and methods to identify an efficient feature selection and classification of electroencephalograph (EEG) data, recent studies emphasize on the development of ensemble learning methods to efficiently classify EEG signals in effective detection of epileptic seizures. Since EEG signals are non-stationary, traditional machine learning approaches may not suffice in effective identification of epileptic seizures. The paper proposes a hybrid ensemble learning framework that systematically combines pre-processing methods with ensemble machine learning algorithms. Specifically, principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) combined along k-means clustering followed by ensemble learning such as extreme gradient boosting algorithms (XGBoost) or random forest is considered. Selection of ensemble learning methods is justified by comparing the mean average precision score with well known methodologies in epileptic seizure detection domain when applied to real data set. The proposed hybrid framework is also compared with other simple supervised machine learning algorithms with training set of varying size. Results suggested that the proposed approach achieves significant improvement in accuracy compared with other algorithms and suggests stability in classification accuracy even with small sized data.
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Shah, Syed Yaseen, Hadi Larijani, Ryan M. Gibson, and Dimitrios Liarokapis. "Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals." Sensors 22, no. 7 (March 23, 2022): 2466. http://dx.doi.org/10.3390/s22072466.
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Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.
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OSORIO, IVAN. "AUTOMATED SEIZURE DETECTION USING EKG." International Journal of Neural Systems 24, no. 02 (January 29, 2014): 1450001. http://dx.doi.org/10.1142/s0129065714500014.
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Changes in heart rate, most often increases, are associated with the onset of epileptic seizures and may be used in lieu of cortical activity for automated seizure detection. The feasibility of this aim was tested on 241 clinical seizures from 81 subjects admitted to several Epilepsy Centers for invasive monitoring for evaluation for epilepsy surgery. The performance of the EKG-based seizure detection algorithm was compared to that of a validated algorithm applied to electrocorticogram (ECoG). With the most sensitive detection settings [threshold T: 1.15; duration D: 0 s], 5/241 seizures (2%) were undetected (false negatives) and with the highest [T: 1.3; D: 5 s] settings, the number of false negative detections rose to 34 (14%). The rate of potential false positive (PFP) detections was 9.5/h with the lowest and 1.1/h with the highest T, D settings. Visual review of 336 ECoG segments associated with PFPs revealed that 120 (36%) were associated with seizures, 127 (38%) with bursts of epileptiform discharges and only 87 (26%) were true false positives. Electrocardiographic (EKG)-based seizure onset detection preceded clinical onset by 0.8 s with the lowest and followed it by 13.8 s with the highest T, D settings. Automated EKG-based seizure detection is feasible and has potential clinical utility given its ease of acquisition, processing, high signal/noise and ergonomic advantages viz-a-viz EEG (electroencephalogram) or ECoG. Its use as an "electronic" seizure diary will remedy in part, the inaccuracies of those generated by patients/care-givers in a cost-effective manner.
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Choubey, Hemant, Sandeep Sharma, Rajendra Bahadur Singh, and Vimlesh Kumar Ray. "HFD and MCFET Based Feature Extraction Technique for Detection of Epilepsy Using ANN Classifier." Traitement du Signal 39, no. 2 (April 30, 2022): 695–700. http://dx.doi.org/10.18280/ts.390233.
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A neurological disorder called Epilepsy which causes the sudden occurrence of epileptic seizures. The electroencephalogram (EEG) is the recorded electrical activities of the brain to examine the epileptic patient through EEG pattern for diagnosis. Epileptic seizure is one of the abnormality or brain disorder in which seizure patterns shows large spikes for specific time domain or area. This work mainly focused on detecting the Epileptic seizures or Epilepsy through the extracted feature like Higuchi Fractal Dimension (HFD) and Masking and Check-in based feature extraction technique (MCFET). Three scaling features of HFD viz. fractal dimension, the standard deviation of fractal dimension and scaling factor while twenty masking and check-in-based features of the upper and the lower envelope along with ten features of the Discrete Wavelet Transform (DWT) coefficients (Table 1) from raw EEG signals are required as input to the Artificial Neural Network (ANN) for classifications. The overall performance is improved in terms of Accuracy, Sensitivity, Specificity through both HFD and MCFET features. Further, the overall accuracy using HFD and MCFET based feature extraction technique around 98% with a bit of computational time of about 1 second by reducing the training percent from 80% to 60%.
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Lasefr, Zakareya, Khaled Elleithy, Ramasani Rakesh Reddy, Eman Abdelfattah, and Miad Faezipour. "An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development." Applied Sciences 13, no. 17 (August 24, 2023): 9571. http://dx.doi.org/10.3390/app13179571.
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Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human–Computer Interaction fields, majorly in the Brain–Computer Interaction (BCI) applications.
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Natu, Milind, Mrinal Bachute, Shilpa Gite, Ketan Kotecha, and Ankit Vidyarthi. "Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches." Computational and Mathematical Methods in Medicine 2022 (January 20, 2022): 1–17. http://dx.doi.org/10.1155/2022/7751263.
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Epileptic seizures occur due to brain abnormalities that can indirectly affect patient’s health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world’s population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to “pops” in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.
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Alam, Md Niyaz, Jainendra Jain, Azhar Danish Khan, Rahul Kaushik, Najam Ali Khan, and Lubhan Singh. "Devices used for Treatment of Epilepsy." INTERNATIONAL JOURNAL OF PHARMACEUTICAL EDUCATION AND RESEARCH (IJPER) 2, no. 02 (December 30, 2020): 56–60. http://dx.doi.org/10.37021/ijper.v2i2.5.
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Epilepsy is a group of chronic neurological disorder of the brain that affects around 1-2% of the population across the world. According to the World Health Organization (WHO), epilepsy is characterized by periodically spontaneous seizures, which are usually due to excessive electrical discharges in a group of brain cells. The earlier day electroencephalogram (EEG) signals are useful tool for detection of epileptic seizures.Epileptogenesis is a slow process. After several months of initial insult, spontaneous recurrent seizures begin to appear. Epilepsy is considered to be resolved for individuals who are seizure-free for the last 10 years, with no seizure medicines for the last 5 years. Currently, used drugs available for treating epilepsy have draw backs like Epileptogenesis and other dose-related side effects. In spite of daily treatment, nearly 30% of patients continue to have convulsions and fail to provide a complete cure. Hence, there is a need for another alternative option to control the epileptic seizure and minimize the duration of seizure without taking a medicine and improving the quality of patient’s life. In current scenario the Vagus nerve stimulation (VNS) has become an important tool for controlling the epileptic seizure. Vagus nerve stimulation is used for patient with refractory and drug resistant epilepsy. Various non-drug therapies form preclinical to clinical for controlling seizures in epileptic patients with drug resistance current available have been highlighted in this review.
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Alam, Md Niyaz, Jainendra Jain, Azhar Danish Khan, Rahul Kaushik, Najam Ali Khan, and Lubhan Singh. "Devices used for Treatment of Epilepsy." INTERNATIONAL JOURNAL OF PHARMACEUTICAL EDUCATION AND RESEARCH (IJPER) 2, no. 02 (December 30, 2020): 56–60. http://dx.doi.org/10.37021/ijper.v2i2.5.
Full textAbstract:
Epilepsy is a group of chronic neurological disorder of the brain that affects around 1-2% of the population across the world. According to the World Health Organization (WHO), epilepsy is characterized by periodically spontaneous seizures, which are usually due to excessive electrical discharges in a group of brain cells. The earlier day electroencephalogram (EEG) signals are useful tool for detection of epileptic seizures.Epileptogenesis is a slow process. After several months of initial insult, spontaneous recurrent seizures begin to appear. Epilepsy is considered to be resolved for individuals who are seizure-free for the last 10 years, with no seizure medicines for the last 5 years. Currently, used drugs available for treating epilepsy have draw backs like Epileptogenesis and other dose-related side effects. In spite of daily treatment, nearly 30% of patients continue to have convulsions and fail to provide a complete cure. Hence, there is a need for another alternative option to control the epileptic seizure and minimize the duration of seizure without taking a medicine and improving the quality of patient’s life. In current scenario the Vagus nerve stimulation (VNS) has become an important tool for controlling the epileptic seizure. Vagus nerve stimulation is used for patient with refractory and drug resistant epilepsy. Various non-drug therapies form preclinical to clinical for controlling seizures in epileptic patients with drug resistance current available have been highlighted in this review.
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Bazil, Carl. "Sleep and Epilepsy." Seminars in Neurology 37, no. 04 (August 2017): 407–12. http://dx.doi.org/10.1055/s-0037-1604352.
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AbstractThe neurophysiology of the brain is complicated and nuanced. It is responsible for the normal sleep/wake states that every person experiences, and for the changes in brain neurophysiology that result in epileptic seizures and in disorders of sleep. It is therefore not surprising that sleep, sleep disorders, and epilepsy interact on many levels. The sleep state influences the detection of interictal epileptiform discharges, important for diagnosis of epilepsy. The state of sleep also influences whether a seizure will occur at a given time, and this differs considerably for various epilepsy syndromes. Sleep disruption of any kind, including from sleep disorders, can worsen epileptic seizures and contribute to intractability. Finally, anticonvulsant medications can influence sleep and sleep disorders in both positive and negative ways. Understanding this interplay between epilepsy and sleep is helpful in the optimal treatment of all patients with epileptic seizures.
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Shah, Syed Yaseen, Hadi Larijani, Ryan M. Gibson, and Dimitrios Liarokapis. "Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition." Applied Sciences 14, no. 2 (January 10, 2024): 599. http://dx.doi.org/10.3390/app14020599.
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An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively.
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Baskar, K., and C. Karthikeyan. "Review on diverse approaches used for epileptic seizure detection using EEG signals." Bangladesh Journal of Medical Science 17, no. 4 (September 19, 2018): 526–31. http://dx.doi.org/10.3329/bjms.v17i4.38307.
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Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal. This detection by the expert clinicians is considered sensitive to bias and time consuming. Further, it suffers from various problems like unsustainability in larger dataset processing and low power detection. Hence, many computerized detection approaches are highly preferred to eliminate the aforementioned problems and to expedite the research in epilepsy seizure detection for aiding the medical professionals. Many such automated epilepsy diagnosis framework has been designed by various researches, which is made to operate in a single or in a combined manner with other domains. This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures. Further, this study summarizes various methods used previously to analyze the epilepsy and seizures based on its state of art approach. Also, investigations are carried out in terms of performance evaluation to find the best suitable epileptic seizure detection technique in the application of Neuro-informatics.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.526-531
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Ahammad, Nabeel, Thasneem Fathima, and Paul Joseph. "Detection of Epileptic Seizure Event and Onset Using EEG." BioMed Research International 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/450573.
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This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds.
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Alwindawi, Alla Fikrat, Osman Nuri UÇAN, and Ameer Hussein Morad. "Wearable Detection Systems for Epileptic Seizure: A review." Al-Khwarizmi Engineering Journal 16, no. 2 (June 1, 2020): 1–13. http://dx.doi.org/10.22153/kej.2020.03.001.
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The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.
Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.
The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple body signals, new algorithm methods, and detection devices that are commercially available.
As a result, the reviewing process shows that there are many research articles that have covered wearable seizure detection systems that based on body signals. The more effective monitoring and detection seizure system is the system that uses multi-body signals, is highly comfortable and has low power consumption.
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Kalitzin, Stiliyan. "Adaptive Remote Sensing Paradigm for Real-Time Alerting of Convulsive Epileptic Seizures." Sensors 23, no. 2 (January 14, 2023): 968. http://dx.doi.org/10.3390/s23020968.
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Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic–clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system.
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Del Giudice, Ennio, Angela Francesca Crisanti, and Alfonso Romano. "Short duration outpatient video electroencephalographic monitoring: The experience of a southern‐Italian general pediatric department." Epileptic Disorders 4, no. 3 (September 2002): 197–202. http://dx.doi.org/10.1684/j.1950-6945.2002.tb00493.x.
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ABSTRACT The authors assessed the event detection rate and clinical usefulness of short duration, outpatient video electroencephalographic monitoring (VEM), in the pediatric age group. The duration of monitoring was set at a two‐hour period. One hundred consecutive patients aged 0‐18 years were enrolled in the study. Patients belonged to one of the following groups: A) patients evaluated to differentiate between true epileptic seizures and nonepileptic events; B) patients with known epilepsy evaluated for a better definition of their seizure type; C) patients with isolated EEG abnormalities evaluated to identify unrecognised, subtle seizures. An additional group D, included patients with enhancement of spike activity induced by sleep. Eighty‐seven patients experienced at least one event per week and 13% had less than one event weekly. The event detection rate was 53% overall, and 61% in the first group of 87 patients. In patients who had events recorded and characterized, epileptic seizures were identified in 37 children (69.8%), and non‐epileptic events in 19 children (35.8%). Diagnostic yield was especially high in children with mental retardation who had predominantly non‐epileptic events. VEM was judged successful and/or informative in 73 cases (73%), and turned out to be useful even in patients with a low baseline frequency of clinical events.
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Gacio, Sebastián. "Amplitude-integrated electroencephalography for neonatal seizure detection. An electrophysiological point of view." Arquivos de Neuro-Psiquiatria 77, no. 2 (February 2019): 122–30. http://dx.doi.org/10.1590/0004-282x20180150.
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ABSTRACT Seizures in the newborn are associated with high morbidity and mortality, making their detection and treatment critical. Seizure activity in neonates is often clinically obscured, such that detection of seizures is particularly challenging. Amplitude-integrated EEG is a technique for simplified EEG monitoring that has found an increasing clinical application in neonatal intensive care. Its main value lies in the relative simplicity of interpretation, allowing nonspecialist members of the care team to engage in real-time detection of electrographic seizures. Nevertheless, to avoiding misdiagnosing rhythmic artifacts as seizures, it is necessary to recognize the electrophysiological ictal pattern in the conventional EEG trace available in current devices. The aim of this paper is to discuss the electrophysiological basis of the differentiation of epileptic seizures and extracranial artifacts to avoid misdiagnosis with amplitude-integrated EEG devices.
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Liu, Yang, Jiang Wang, Lihui Cai, Yingyuan Chen, and Yingmei Qin. "Epileptic seizure detection from EEG signals with phase–amplitude cross-frequency coupling and support vector machine." International Journal of Modern Physics B 32, no. 08 (March 13, 2018): 1850086. http://dx.doi.org/10.1142/s0217979218500868.
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As a pattern of cross-frequency coupling (CFC), phase–amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB–MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6[Formula: see text]s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.
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Kuznetsova, Aleksandra A., Inna O. Shchederkina, Mikhail V. Sinkin, and Valeriy V. Gorev. "Epileptic seizures and epilepsy in children after stroke." L.O. Badalyan Neurological Journal 4, no. 1 (April 20, 2023): 29–42. http://dx.doi.org/10.46563/2686-8997-2023-4-1-29-42.
Full textAbstract:
Despite the low prevalence of cerebrovascular diseases in childhood, up to 60% of children realize acute symptomatic epileptic seizures and up to 30% of patients develop post-stroke epilepsy in the long-term period. The lack of a unified terminology and temporal criteria for both acute symptomatic epileptic seizures and post-stroke epilepsy complicates the analysis of the studied groups. Many studies are limited to small groups, proprietary terminology, different age medians, and inclusion criteria. Both clinical and instrumental risk factors for the development of post-stroke epilepsy in childhood have not been identified, which makes it difficult to identify risk groups and predict the outcome in the long term period. The only significant risk factor in most publications is «younger age», but its boundaries are blurred. Most studies are retrospective, which may affect the incidence and type of epileptic seizures in acute cerebrovascular accident. Electroencephalography is the most informative method for detecting subclinical changes and identifying risk groups. Continuous electroencephalography is effective primarily in the detection of non-convulsive status epilepticus. Currently, there are no unified protocols for examining stroke patients, both at the onset and in follow-up. The search for articles was carried out in the scientific platforms PubMed, Google Scholar, eLIBRARY.
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Li, Yang, Weigang Cui, Meilin Luo, Ke Li, and Lina Wang. "Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features." International Journal of Neural Systems 28, no. 07 (July 18, 2018): 1850003. http://dx.doi.org/10.1142/s012906571850003x.
Full textAbstract:
The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.
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Farooq, Muhammad Shoaib, Aimen Zulfiqar, and Shamyla Riaz. "Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges." Diagnostics 13, no. 6 (March 10, 2023): 1058. http://dx.doi.org/10.3390/diagnostics13061058.
Full textAbstract:
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
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Joy, Avijit Dey, Sraboni Sarkar, and Abul Kalam Azad. "Detection of Epileptic Seizures from EEG Signals Using Machine Learning Classifiers." Bangladesh Journal of Medical Physics 15, no. 1 (December 25, 2022): 28–42. http://dx.doi.org/10.3329/bjmp.v15i1.63560.
Full textAbstract:
Epileptic seizure is a chronic neurological disorder which affects millions of people all over the globe. It can be treated in a better way if the symptoms are detected at an early stage. In this study, we have demonstrated and evaluated the classification performances of different machine learning classifiers for the detection of epileptic seizures from electroencephalography (EEG) signals. For this, we have first applied principal component analysis (PCA) on EEG signals to obtain much reduced-length PCA vectors. These vectors are then applied to decision tree (DT), k-nearest neighbor (KNN), Naïve Bayes (NB), support vector machine (SVM) and artificial neural network (ANN) classifiers for the detection of epileptic seizures. The effects of length of PCA vectors on the performances of these classifiers have also been analyzed rigorously for 2-class, 3-class and 5-class classification of EEG signals. Besides such PCA-based classifiers, we have also proposed and evaluated the performances of a customized convolutional neural network (CNN) to directly extract features from the EEG signals as well as to perform classification tasks. The results showed that CNN outperforms PCA-based machine learning classifiers. For 2-class classification cases, CNN attains classification accuracies in the range from 99.50% to 100%, whereas 98.48% and 96.32% accuracies are obtained with CNN for 3-class and 5-class classification cases. The results signify that the proposed CNN classifier can be considered as a highly-efficient scheme for the reliable detection of epileptic seizures from EEG signals.
Bangladesh Journal of Medical Physics Vol.15 No.1 2022 P 28-42
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Gaidar, V. "Machine learning for epilepsy detection and forecast review: new challenges and perspectives." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 4 (2018): 98–101. http://dx.doi.org/10.17721/1812-5409.2018/4.14.
Full textAbstract:
The comparative analysis of machine learning methods has performed to solve the problem of early detection and prediction of epileptic seizures using electroencephalographic signals. Recent studies has shown that it is possible to predict seizures in prior of its physical appearance. Our goal is to present and analyse different approaches of seizure prediction techniques, particulary in machine learning and deep learning. Seizure prediction has made important advances over the last decade, nevertheless it is still a problem to provide steady algorithm of seizure early detection. Also, within individual patients exhibit distinctive dynamics, is it cruicial to find algorithms providing greater clinical utility. This article focuses of the problem of features development from electroencephalography signals in order to provide the accurate pattern recognition techniques for detection and classification of epilepsy seizures in advance. The mathematical model of the algorithms is constructed and quantitative data presented for estimating the methods efficiency.
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Sun, Qi, Yuanjian Liu, and Shuangde Li. "Automatic Seizure Detection Using Multi-Input Deep Feature Learning Networks for EEG Signals." Journal of Sensors 2024 (February 5, 2024): 1–15. http://dx.doi.org/10.1155/2024/8835396.
Full textAbstract:
Epilepsy, a neurological disease associated with seizures, affects the normal behavior of human beings. The unpredictability of epileptic seizures has caused great obstacles to the treatment of the disease. The automatic seizure detection method based on electroencephalogram (EEG) can assist experts in predicting seizures to improve treatment efficiency. Epileptic seizure detection cannot be achieved accurately using the single-view characteristics of the signals. Moreover, manual feature extraction is a time-consuming task. To design a high-performance seizure identification method, automatic learning of multi-view features becomes an indispensable part for seizure detection. Therefore, the paper proposes a multi-input deep feature learning networks (MDFLN) model, which comprehensively considers the features from the time domain and the time–frequency (TF) domain for EEG signals. The MDFLN model automatically extracts the feature information of the signals through deep learning networks. Then, the bidirectional long short-term memory (BLSTM) network is used to distinguish seizure and nonseizure events. Furthermore, the effectiveness of the proposed network structure is verified in two public datasets. The experimental results demonstrate that the classification accuracy of the proposed method based on multi-view features is at least 2.2% higher than the single-view features. The MDFLN achieves better performance on CHB-MIT and Bonn datasets with accuracy of 98.09% and 98.4%, respectively. The fine-tuned model with the validation set also improves the classification performance. Compare with the state-of-the-art seizure detection methods, the multi-input deep learning network has superior competence with high sensitivity on the CHB-MIT dataset. The proposed automatic seizure detection method can reduce time consumption and effectively assist experts in the clinical diagnosis and treatment.