Advancements in epilepsy classification: Current trends and future directions
This paper presents a comprehensive survey on categorizing focal and non-focal epilepsy using Electroencephalogram (EEG) signals. It emphasizes how recent advances in machine learning and deep learning methodologies assists in overcoming the existing challenges in classification. The paper synthesiz...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125001037 |
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| author | Rajani Rai B Karunakara Rai B Mamatha A S Kavitha Sooda |
| author_facet | Rajani Rai B Karunakara Rai B Mamatha A S Kavitha Sooda |
| author_sort | Rajani Rai B |
| collection | DOAJ |
| description | This paper presents a comprehensive survey on categorizing focal and non-focal epilepsy using Electroencephalogram (EEG) signals. It emphasizes how recent advances in machine learning and deep learning methodologies assists in overcoming the existing challenges in classification. The paper synthesizes cutting-edge techniques with the focus on the application of hybrid models that combine traditional signal processing techniques with machine learning algorithms. By highlighting key breakthroughs in the field, the paper aims to propose novel directions for improving classification precision. Furthermore, the paper delves into the challenges faced by current methods and the possible solutions. The paper concludes with the discussion on potential future research directions, especially in areas of multimodal data integration and real-time seizure prediction, and emphasizes the potential for AI-driven personalized epilepsy treatment techniques. |
| format | Article |
| id | doaj-art-16d6eece41fa475dbb65ec0f6e360cc0 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-16d6eece41fa475dbb65ec0f6e360cc02025-08-20T03:30:29ZengElsevierMethodsX2215-01612025-06-011410325710.1016/j.mex.2025.103257Advancements in epilepsy classification: Current trends and future directionsRajani Rai B0Karunakara Rai B1Mamatha A S2Kavitha Sooda3Dept.of ECE, Vivekananda College of Engineering and Technology, Puttur, D.K., India; Corresponding author.Dept. of ECE, Nitte Meenakshi Institute of Technology, Bangalore, IndiaDept. of ECE, NITTE (Deemed to be University, NMAM Institute of Technology, Nitte, IndiaDept. of CSE, BMS College of Engineering, Bangalore, IndiaThis paper presents a comprehensive survey on categorizing focal and non-focal epilepsy using Electroencephalogram (EEG) signals. It emphasizes how recent advances in machine learning and deep learning methodologies assists in overcoming the existing challenges in classification. The paper synthesizes cutting-edge techniques with the focus on the application of hybrid models that combine traditional signal processing techniques with machine learning algorithms. By highlighting key breakthroughs in the field, the paper aims to propose novel directions for improving classification precision. Furthermore, the paper delves into the challenges faced by current methods and the possible solutions. The paper concludes with the discussion on potential future research directions, especially in areas of multimodal data integration and real-time seizure prediction, and emphasizes the potential for AI-driven personalized epilepsy treatment techniques.http://www.sciencedirect.com/science/article/pii/S2215016125001037ArtifactsClassifiersEEGFeature extractionFocalNon-focal |
| spellingShingle | Rajani Rai B Karunakara Rai B Mamatha A S Kavitha Sooda Advancements in epilepsy classification: Current trends and future directions MethodsX Artifacts Classifiers EEG Feature extraction Focal Non-focal |
| title | Advancements in epilepsy classification: Current trends and future directions |
| title_full | Advancements in epilepsy classification: Current trends and future directions |
| title_fullStr | Advancements in epilepsy classification: Current trends and future directions |
| title_full_unstemmed | Advancements in epilepsy classification: Current trends and future directions |
| title_short | Advancements in epilepsy classification: Current trends and future directions |
| title_sort | advancements in epilepsy classification current trends and future directions |
| topic | Artifacts Classifiers EEG Feature extraction Focal Non-focal |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125001037 |
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