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9 results for “Seizure Detection”
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Research ArticleOpen accessPhrase in the titleFine-Tuned and Optimized Transformer-Based Model for EEG Seizure DetectionPuspanjali Mallik, Ajit Kumar Nayak, Kumar Janardan Patra et al.Epilepsy, a widely recognized neurological disorder, results from irregularities in the transmission of electrical impulses among neurons in the brain. Over the last two decades, significant efforts have been made by researchers and clinicians to develop effective methods for its early detection and management. The electroencephalogram (EEG), a non-invasive tool used to monitor brainwave activity, has become a central device … …Journal of Computer Science, Vol. 21, Issue 11 · 11 Feb 2026 · 1,412 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleEpileptic Seizure Detection Using Integrated Decomposed Features from EEG SignalShupta Das, Suraiya Akter Mumu, M. A. H. Akhand, Md Abdus Samad KamalThe root cause of the seizure is a sudden abnormal excessive electrical discharge in the brain and it is an Epileptic Seizure (ES) when such abnormal electrical activity arises particularly for epilepsy. Recognizing ES is crucial for effective treatment as it often repeats and can lead to serious outcomes. Since epilepsy is a neurological issue, detecting ES by analyzing brain … …Journal of Computer Science, Vol. 20, Issue 10 · 8 Aug 2024 · 4,845 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleEpileptic Seizure Detection from EEG Signal Using Progressive Channel Selection and Deep LearningSuraiya Akter Mumu, Shupta Das, M. A. H. Akhand et al.Epileptic Seizures (ES), marked by abrupt abnormal electrical discharges in the brain, represent the primary symptom of epilepsy, making timely and accurate detection critical because of their unpredictable recurrence and potentially severe consequences. The Electroencephalogram (EEG), a widely used, non-invasive, and economical method for monitoring brain activity, plays a key role in automated ES detection through Machine Learning (ML) and … …Journal of Computer Science, Vol. 22, Issue 6 · 8 Jul 2026 · 1,393 accessesView PDFCite
Research ArticleOpen accessIn the keywordsIntegrating (OLAP) Online Analytical Processing With a Fast Algorithm for Epileptic Seizures DetectionIlyas Zidane, Wajih Rhalem, Nordine ZidaneEpilepsy is a neurological disorder characterized by recurrent seizures, significantly impacting the quality of life of patients. Effective detection and analysis of epileptic seizures are crucial for timely intervention and management. This paper presents the integration of a fast algorithm for epileptic seizures detection with an OLAP (Online Analytical Processing) system using a star schema data warehouse model. By leveraging … …Journal of Computer Science, Vol. 22, Issue 10 · 12 Oct 2026 · 678 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph RecordsHeri Kuswanto, Mutiah Salamah, Muhammad Idrus FachruddinComplexity in data structure has led to the rapid development of computational statistics methods. Machine learning approaches have been introduced and applied to solve complex problems in many fields. This paper applies two common machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), in the detection of epilepsy. The diagnosis of epilepsy can usually only be made when … …American Journal of Applied Sciences, Vol. 14, Issue 5 · 4 May 2017 · 8,134 accessesView PDFCite
Research ArticleOpen accessIn the abstractSpatial Interaction Image of Electroencephalography Signal during Epileptic Seizure on Flat ElectroencephalographyGoh Chien Yong, Tahir Ahmad, Normah MaanElectroencephalogram (EEG) is widely used for recording the brain electrical activity. It plays an important role in the detection and classification of epilepsy seizures. In addition, it is believed to possess the ability to detect the location of epileptic focus to some degree. In this study, an EEG data during seizure obtained is analysed and transformed into an image form, … …Journal of Mathematics and Statistics, Vol. 13, Issue 1 · 13 Mar 2017 · 6,899 accessesView PDFCite
Research ArticleOpen accessIn the abstractDETERMINATION OF EPILEPTIC DISORDER WITH DISCRETE WAVELET TRANSFORMS AND NEURAL NETWORK CLASSIFIERS. T. Sadish Kumar, N. KasthuriNowadays Epileptic disorder is a most challenge aspects in brain activation. Electroencephalograph (EEG) is one of the popular procedures to understand the human brain condition. The activation of brain will be changed due to the symptoms of neurological disorder. We have been proposed a procedure to find epilepsy disorder, using discrete wavelet transform and neural network classifier. The EEG classification … …Journal of Computer Science, Vol. 10, Issue 1 · 12 Nov 2013 · 6,657 accessesView PDFCite
Research ArticleOpen accessIn the abstractSwarm Negative Selection Algorithm for Electroencephalogram Signals ClassificationNasser Omer Sahel Ba-Karait, Siti Mariyam Shamsuddin, Rubita SudirmanProblem statement: The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust … …Journal of Computer Science, Vol. 5, Issue 12 · 31 Dec 2009 · 7,205 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy FilterAini Hussain, Rosniwati Ghafar, Salina Abdul Samad, Nooritawati Md TahirProblem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals … …Journal of Computer Science, Vol. 5, Issue 7 · 31 Jul 2009 · 7,326 accessesView PDFCite