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Relevant bibliographies by topics / Epileptic seizures detection

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Academic literature on the topic 'Epileptic seizures detection'

Author: Grafiati

Published: 25 May 2024

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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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Dissertations / Theses on the topic "Epileptic seizures detection"

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McGroggan, N. "Neutral network detection of epileptic seizures in the electroencephalogram." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249426.

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Valko, Andras, and Antoine Homsi. "Predictive detection of epileptic seizures in EEG for reactive care." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15078.

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It is estimated that 65 million people worldwide have epilepsy, and many of them have uncontrollable seizures even with the use of medication. A seizure occurs when the normal electrical activity of the brain is interrupted by sudden and unusually intense bursts of electrical energy, and these bursts can be observed and detected by the use of an electroencephalograph (EEG) machine. This work presents an algorithm that monitors subtle changes in scalp EEG characteristics to predict seizures. The algorithm is built to calibrate itself to every specifc patient based on recorded data, and is computationally effcient enough for future on-line applications. The presented algorithm performs ICA-based artifact filtering and Lasso-based feature selection from a large array of statistical features. Classification is based on a neural network using Bayesian regularized backpropagation.The selected method was able to classify 4 second long preictal segments with an average sensitivity of 99.53% and an average specificity of 99.9% when tested on 15 different patients from the CHB-MIT database.

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PISANO, BARBARA. "Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals." Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255953.

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Epilepsy is one of the major neurological disorders that affects more than 50 million people around the world; it is characterized by unpredictable seizures due to an abnormal electrical activity in the brain. In this thesis nocturnal epilepsy has been investigated. In particular, Nocturnal Frontal Lobe Epilepsy (NFLE), that is a form of epilepsy in which seizures occur predominantly during sleep with symptoms including nocturnal awakenings, dystonic and tonic postures and clonic limb convulsions. The electroencephalographic (EEG) signals, which record the electrical activity of the brain, are used by neurologists to diagnose epilepsy. However, in almost 50% of NFLE cases, the EEG does not show abnormality during seizures, making the neurologists work to identify the epileptic events very difficult, thereby requiring the support of video recording to verify the epileptic events, with a subsequent time-consuming procedure. In literature few scientific contributions address the classification of nocturnal epileptic seizures. In this thesis, the automatic systems, both customized for single patient and generalized have been developed to find the best nocturnal epileptic seizure detection system from EEG signals. The combination of feature extraction and selection methods, associated to classification models based on Self Organizing Map (SOM), have been investigated following the classical machine learning approach. The ability of SOM to represent data from a high-dimensional space in a low-dimensional space, preserving the topological properties of the original space, has been exploited to identify nocturnal epileptic seizures and track the temporal projection of the EEG signals on the map. The proposed methods allow the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering data coming from seizure and non-seizure states. The results obtained show that the patient-specific system achieves better performance than a patient-independent system. Moreover, comparing the performances with those of a binary classifier, widely used in epileptic seizure detection problems, the Support Vector Machine (SVM), the SOM model achieves good and, for some patients, higher performances. In particular, the patient-customized system using SOM model, reaches an average value of sensitivity and specificity equal to 82.85% and 89.92%, respectively; whereas the SVM classifier achieved an average sensitivity and specificity equal to 82.11% and 82.85%, respectively, suggesting the use of SOM model as a good alternative for nocturnal epileptic seizure detection. The discriminating power of SOM and the possibility to follow the temporal sequence of the EEG recordings on the map can provide information on an imminent epileptic seizure, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures.

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Fan, Xiaoya. "Dynamics underlying epileptic seizures: insights from a neural mass model." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/279546/6/contratXF.pdf.

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In this work, we propose an approach that allows to explore the potential pathophysiological mechanisms (at neuronal population level) of ictogenesis by combining clinical intracranial electroencephalographic (iEEG) recordings with a neural mass model. IEEG recordings from temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters (average synaptic gains of the excitatory, slow and fast inhibitory population, Ae, B and G) were identified during interictal to ictal transition. We analyzed the temporal evolution of four ratios, i.e. Ae/G, Ae/B, Ae/(B + G), and B/G. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, suggesting the disturbance and restoration of balance between excitation and inhibition around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on the breakdown of excitation/inhibition balance. Results confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. We further explored the distribution of the average synaptic gains in parameter space and their temporal evolution, i.e. the path through the model parameter space, in TLE patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during ictal and returned when the seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from individual patients. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing an identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy. The increase of excitation/inhibition ratios, i.e. Ae/G, Ae/B and Ae/(B+G), around seizure onset makes them potential cues for seizure detection. We explored the feasibility of a model based seizure detection algorithm. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on iEEG samples from patients suffering from different types of epilepsy. Results suggest that Ae/(B+G), i.e. excitation/(slow + fast inhibition) ratio, allowed the best performance and that the algorithm best suited TLE patients. Leave-one-out cross-validation showed that the algorithm achieved 94.74% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was -1.0 s. Of interest, the values of the threshold determined by leave-one-out cross-validation for TLE patients were quite constant, suggesting a general excitation/inhibition balance baseline in background iEEG among TLE patients. Such a model-based seizure detection approach is of clinical interest and could also achieve good performance for other types of epilepsy provided that more appropriate model, i.e. better describe epileptic EEG waveforms for other types of epilepsy, is implemented. Altogether, this thesis contributes to the field of epilepsy research from two perspectives. Scientifically, it gives new insights into the mechanisms underlying interictal to ictal transition, and facilitates better understanding of epileptic seizures. Clinically, it provides a tool for reviewing EEG data in a more efficient and objective manner and offers an opportunity for on-demand therapeutic devices.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

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Shahidi, Zandi Ali. "Scalp EEG quantitative analysis : automated real-time detection and prediction of epileptic seizures." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42748.

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As a chronic neurological disorder, epilepsy is associated with recurrent, unprovoked epileptic seizures resulting from a sudden disturbance of brain function. Long-term monitoring of epileptic patients' Electroencephalogram (EEG) is often needed for diagnosis of seizures, which is tedious, expensive, and time-consuming. Also, clinical staff may not identify the seizure early enough to determine the semiology at the onset. This motivates EEG-based automated real-time detection of seizures. Apart from their possible severe side effects, common treatments for epilepsy (medication and surgery) fail to satisfactorily control seizures in ~25% of patients. EEG-based seizure prediction systems would significantly enhance the chance of controlling/aborting seizures and improve safety and quality of life for patients. This thesis proposes novel EEG-based patient-specific techniques for real-time detection and prediction of epileptic seizures and also presents a pilot study of scalp EEGs acquired in a unique low-noise underground environment. The proposed detection method is based on the wavelet packet analysis of EEG. A novel index, termed the combined seizure index, is introduced which is sensitive to both the rhythmicity and relative energy of the EEG in a given channel and considers the consistency among different channels at the same time. This index is monitored by a cumulative sum procedure in each channel. This channel-based information is then used to generate the final seizure alarm. In this thesis, a prediction method based on a variational Bayesian Gaussian mixture model of the EEG positive zero-crossing intervals is proposed. Novel indices of similarity and dissimilarity are introduced to compare current observations with the preictal and interictal references and monitor the changes for each channel. Information from individual channels is finally combined to trigger an alarm for upcoming seizures. These methods are evaluated using scalp EEG data. The prediction method is also tested against a random predictor. Finally, this thesis investigates the capability of an ultra-shielded underground capsule for acquiring clean EEG. Results demonstrate the potential of the capsule for novel EEG studies, including establishing novel low-noise EEG benchmarks which could be helpful in better understanding of the brain functions and mechanisms deriving various brain disorders, such as epilepsy.

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Gheryani, Mostafa. "Epileptic seizure and anomaly detection in internet of medical things." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5211.

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L'objectif de ma thèse est d'analyser les caractéristiques des signaux inertiels et physiologiques qui générés par les mouvements inhabituels des patients lorsque la crise survient et de développer un algorithme pour détecter la crise. Notre approche dans le chapitre III commence par dériver la moyenne quadratique pour l'ACM et le Gyro, suivie de la normalisation de signaux entiers dans la même plage puis de l'agrégation en un seul signal. Le contrôle du graphique avec ses limites supérieure et inférieure est défini lors de la phase au repos et utilisé pour détecter les crises anormales et pour déclencher une alarme. La procédure dans le chapitre IV de détection s'exécute dans un dispositif de collecte de données portable et déclenche une alarme. Cet algorithme est basé sur la dérivation des mesures instantanées dans une plage de données glissante contenant des mesures inertielles de 3D (ACM), 3D Gyro et de EMG. La différence entre la puissance estimée et la puissance mesurée est utilisé comme entrée pour l'algorithme de détection basé sur la carte de contrôle de Shewhart. Lorsque la différence entre la puissance prévue et la puissance dérivée dépasse les limites [limite inférieure/supérieure] pour plusieurs créneaux consécutifs, une alarme est déclenchée. L'approche que nous proposons permet une bonne détection avec un FAR de 4\% et une sensibilité de 97\%. Notre modèle dans Le chapitre V commence par réduire la dimension des données collectées grâce à l'utilisation de la moyenne quadratique pour dériver un signal de 3D ACM et un signal du 3D Gyro. Avec les 3 signaux dérivés (ACM, Gyro et EMG), nous appliquons le TVP pour dériver un signal utilisé comme entrée pour le mécanisme de détection d'anomalie. La version robuste du z-score est appliquée sur le signal résultant produit pour détecter les déviations associées aux crises avant de déclencher une alarme. Nos résultats expérimentaux montrent que notre approche proposée est robuste contre les mouvements nocturnes et atteint un haut niveau de précision de détection avec un faible FAR. Ensuite, nous comparons les performances de notre approche avec la méthode des passages à zéro calculées à partir de sEMG. Notre approche montre que la précision de détection à l'aide du VTP surpasse le nombre de passages à zéro sur une plage glissante de chevauchement de 1 seconde. Dans le chapitre VI, Les appareils IoMT sont utilisés pour acquérir ACM, Gyro et EMG et pour transmettre les mesures à LPU pour traitement. Lorsque le LPU détecte des changements anormaux dans les mesures, il déclenche une alarme. Notre approche proposée utilise SVM avec option de rejet pour distinguer les crises des activités normales de la vie quotidienne. Les caractéristiques présentant des changements physiologiques de l'activité musculaire et les données inertielles ont été extraites dans LPU et sont utilisées comme entrée pour l'algorithme de détection. L'option de rejet dans SVM est utilisée pour améliorer la fiabilité du système de surveillance et pour réduire les fausses alarmes, où l'utilisateur est averti et a la possibilité de supprimer l'alarme dans son smartphone en l'absence de saisie. Les expériences menées ont prouvé que notre approche proposée peut atteindre une bonne précision pour distinguer les crises des activités normales avec seulement 4% de taux de FAR. Dans chapitre VII, nous proposons un cadre pour empêcher une MitM de perturber les opérations et interdire le déclenchement d'alarmes par le système de surveillance à distance des soins de santé. Pour réduire la consommation d'énergie lours de la transmission normale des données et préserver la confidentialité des données de santé, notre système transmet une signature de plus petite taille dérivée des données acquises avec un code d'authentification de message, où la clé est dérivée de RSSI. Nos résultats expérimentaux montrent que notre approche peut atteindre une précision de détection élevée avec un faible FAR de 3%
The goal of my PhD is to investigate the characteristics of inertial and physiological signals via IoMT systems generated by epileptic seizure and to develop an algorithm to detect the seizure. The focus of the algorithms lies in nocturnal seizures where the risk of SUDEP is high because the patients are unsupervised while sleeping. In chapter III analysis we propose an IoMT platform for seizure detection. The proposed framework approach starts by deriving the RMS for ACM and Gyro, followed by the normalization of whole signals (ACM, Gyro and EMG) in the same range, and aggregate all into one signal. The chart’s control with its upper and lower limits are derived in the training phase and used to detect abnormal seizures and to raise an alarm. In chapter IV Our proposed algorithm is based on deriving instantaneous power measurements in a sliding window containing 3D ACM or 3D Gyro or EMG. The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart Control Chart (SCC). When the difference between forecasted and derived power exceeds chart limits [lower, upper] for several consecutive slots, an alarm is raised. Our proposed approach provides low FAR (4%) and sensitivity of 97%. In Chapter V our proposed method starts by reducing the dimension of collected data using RMS to derive one signal from 3D ACM and one signal from 3D Gyro. With the derived 3 collected signals (ACM, Gyro and EMG), we apply VTP to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm. Our experimental results show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate. Afterward, we compare the performance of our approach with the zero-crossings method calculated from sEMG. Our approach shows that the detection accuracy using VTP outperforms zero-crossing count over an overlapping sliding window of 1 second. In chapter VI, we propose an approach using the IoMT devices to acquire EMG, ACM and Gyro data and to transmit the measurements to a LPU for processing. When the LPU detects abnormal changes in the measurements, it raises an alarm for assistant. Our proposed approach uses SVM with reject option to distinguish seizures from normal daily life activity. Features presenting physiological changes of muscular activity and inertial data were extracted in LPU and are used as input for the detection algorithm. The reject option in SVM is used to enhance the reliability of the monitoring system and to reduce FAR, where the user is notified and can discard the alarm in his smartphone in the absence of seizure. The conducted experiments proved that our proposed approach could achieve a good accuracy with only 4% of false alarm rate. Finally, since we are using IoMT sensors, which are susceptible to data security issues. We proposed a solution to prevent Man in the Middle (MitM) attack, which can identify healthcare emergencies of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this chapter, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from Received Signal Strength Indication (RSSI). Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%

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McNally, Kelly A. "Application of Signal Detection Theory to Verbal Memory Testing for the Differential Diagnosis of Psychogenic Nonepileptic and Epileptic Seizures." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178883120.

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Truong, Nhan Duy. "Epileptic Seizure Detection and Forecasting Ecosystems." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.

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Epilepsy affects almost 1% of the global population and considerably impacts the quality of life of those patients diagnosed with the disease. Ambulatory EEG monitoring devices that can detect or predict seizures could play an important role for people with intractable epilepsy. Many outstanding studies in detecting and forecasting epileptic seizures using EEG have been developed over the past three decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. To achieve high performance, many of these studies relied on handcraft feature extraction. This approach is not generalizable and requires significant modifications for each new patient. This issue greatly limits the applicability of such methods to hardware implementation. In this thesis, we propose a deep learning-based solution for generalized epileptic seizure detection and forecasting that does not require handcraft feature extraction. The method can be applied to any other patient without the need for manual feature extraction. Secondly, we optimize seizure detection and forecasting systems to reduce computational complexity and power consumption. The optimization is performed from two aspects: algorithm and input signal. In the first aspect, we propose two approaches: automatic channel selection to reduce the number of necessary EEG electrodes; Integer-Net, an integer convolutional neural network, to reduce computational complexity and required memory. In the second aspect, we investigate how sensitive seizure detection algorithms are regarding EEG's resolution. Another problem that we would like to address is the lack of labeled EEG data for epilepsy. Today the process of epileptic seizure identification and data labeling is done by neurologists, which is expensive and time-consuming. We propose an unsupervised learning approach to make use of unlabeled EEG data which is more accessible.

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Ramachandran, Ganesan. "Comparison of algorithms for epileptic seizure detection." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000597.

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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.

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Books on the topic "Epileptic seizures detection"

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1959-, Mareels Iven, and Cook Mark 1960-, eds. Epileptic seizures and the EEG: Measurement, models, detection, and prediction. Boca Raton: Taylor & Francis, 2010.

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Varsavsky, Andrea. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis, 2011.

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Cook, Mark, Iven Mareels, and Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.

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Cook, Mark, Iven Mareels, and Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.

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Cook, Mark, Iven Mareels, and Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.

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Cook, Mark, Iven Mareels, and Andrea Varsavsky. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Taylor & Francis Group, 2016.

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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-01888-5.

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Dehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra, and Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology, 2019.

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Dehuri, Satchidananda, Alok Kumar Jagadev, Shruti Mishra, and Sandeep Kumar Satapathy. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier Science & Technology Books, 2019.

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Vespa, Paul M. Electroencephalogram monitoring in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0221.

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Electroencephalography monitoring provides a method for monitoring brain function, which can complement other forms of monitoring, such as monitoring of intracranial pressure and derived parameters, such as cerebral perfusion pressure. Continuous electroencephalogram (EEG) monitoring can be helpful in seizure detection after brain injury and coma. Seizures can be detected by visual inspection of the raw EEG and/or processed EEG data. Treatment of status epilepticus can be improved by rapid identification and abolition of seizures using continuous EEG. Quantitative EEG can also be used to detect brain ischaemia and seizures, to monitor sedation and aid prognosis.

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Book chapters on the topic "Epileptic seizures detection"

1

Sharma, Ayushi, and Sandeep Paul. "Epileptic Seizures Detection." In Lecture Notes in Mechanical Engineering, 309–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8025-3_31.

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Holonec, R., S. Vlad, and L. Rapolti. "Application for Detection of Epileptic Seizures." In 6th International Conference on Advancements of Medicine and Health Care through Technology; 17–20 October 2018, Cluj-Napoca, Romania, 91–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6207-1_15.

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Carrión, Salvador, Álvaro López-Chilet, Javier Martínez-Bernia, Joan Coll-Alonso, Daniel Chorro-Juan, and Jon Ander Gómez. "Automatic Detection of Epileptic Seizures with Recurrent and Convolutional Neural Networks." In Lecture Notes in Computer Science, 522–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_46.

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AbstractComputer-aided diagnosis based on intelligent systems is an effective strategy to improve the efficiency of healthcare systems while reducing their costs. In this work, the epilepsy detection task is approached in two different ways, recurrent and convolutional neural networks, within a patient-specific scheme. Additionally, a detector function and its effects on seizure detection performance are presented. Our results suggest that it is possible to detect seizures from scalp EEGs with acceptable results for some patients, and that the DeepHealth framework is a proper deep learning software for medical research.

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de Bruijne, G. R., P. C. W. Sommen, and R. M. Aarts. "Detection of Epileptic Seizures Through Audio Classification." In IFMBE Proceedings, 1450–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_344.

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Panigrahi, Narayan, and Saraju P. Mohanty. "Detection of Epileptic Seizures from EEG Data." In Brain Computer Interface, 175–85. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003241386-12.

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Moldovan, Dorin. "Scalable Hypothesis Tests for Detection of Epileptic Seizures." In Computational Statistics and Mathematical Modeling Methods in Intelligent Systems, 157–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31362-3_16.

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Nageshwar, V., P. Venkateswara Rao, C. Sarika, K. Manusha, and Y. Deepthi. "Detection of Epileptic Seizures from Logistic Model Trees." In Atlantis Highlights in Computer Sciences, 371–82. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-314-6_36.

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Mehta, Deval, Shobi Sivathamboo, Hugh Simpson, Patrick Kwan, Terence O’Brien, and Zongyuan Ge. "Privacy-Preserving Early Detection of Epileptic Seizures in Videos." In Lecture Notes in Computer Science, 210–19. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43904-9_21.

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Siuly, Siuly, Yan Li, and Yanchun Zhang. "A Novel Clustering Technique for the Detection of Epileptic Seizures." In Health Information Science, 83–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7_5.

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Patan, Krzysztof, and Grzegorz Rutkowski. "Detection of Epileptic Seizures via Deep Long Short-Term Memory." In Advances in Intelligent Systems and Computing, 166–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29885-2_15.

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Conference papers on the topic "Epileptic seizures detection"

1

Mirzaei, Ahmad, Ahmad Ayatollahi, Parisa Gifani, and Leili Salehi. "Spectral Entropy for Epileptic Seizures Detection." In 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2010). IEEE, 2010. http://dx.doi.org/10.1109/cicsyn.2010.84.

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Gupta, Sarthak, Siddhant Bagga, Vikas Maheshkar, and M. P. S. Bhatia. "Detection of Epileptic Seizures using EEG Signals." In 2020 International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2020. http://dx.doi.org/10.1109/aisp48273.2020.9073157.

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Cuppens, Kris, Bart Vanrumste, Berten Ceulemans, Lieven Lagae, and Sabine Van Huffel. "Detection of Epileptic Seizures Using Video Data." In 2010 6th International Conference on Intelligent Environments (IE). IEEE, 2010. http://dx.doi.org/10.1109/ie.2010.77.

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Sharma, Swati, and Arjun Arora. "Detection of Epileptic Seizures using Machine Learning." In 2022 5th International Conference on Advances in Science and Technology (ICAST). IEEE, 2022. http://dx.doi.org/10.1109/icast55766.2022.10039516.

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Kusmakar, Shitanshu, Chandan K. Karmakar, Bernard Yan, Terence J. O'Brien, Ramanathan Muthuganapathy, and Marimuthu Palaniswami. "Onset Detection of Epileptic Seizures From Accelerometry Signal." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513669.

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Padma, Tatiparti, and Ch Usha Kumari. "Sudden Fall Detection and Protection for Epileptic Seizures." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9009317.

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Gupta, Surbhi, Mustafa Sameer, and Neeraj Mohan. "Detection of Epileptic Seizures using Convolutional Neural Network." In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9396983.

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Aldana, Yissel Rodriguez, Borbala Hunyadi, Enrique J. Maranon Reyes, Valia Rodriguez Rodriguez, and Sabine Van Huffel. "Nonconvulsive epileptic seizures detection using multiway data analysis." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081629.

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Naser, Zaman Gheni, and Raid Luaibi Lafta. "EEG and Fractal Dimension for Epileptic Seizures Detection." In 2023 Al-Sadiq International Conference on Communication and Information Technology (AICCIT). IEEE, 2023. http://dx.doi.org/10.1109/aiccit57614.2023.10218011.

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Reyes, C. F., T. J. Contreras, B. Tovar, L. I. Garay, and M. A. Silva. "Detection of absence epileptic seizures using support vector machine." In 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2013. http://dx.doi.org/10.1109/iceee.2013.6676057.

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Reports on the topic "Epileptic seizures detection"

1

Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

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2

Elarton, J., and K. Koepsel. Epileptic Seizure Detection & Warning Device. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/7856.

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