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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques |
| Authors Name: | Dr. K. SubbaRao , DAGGUMATI JOHARIKA , GURRAM SRAVANYA , TIRUVEEDULA LEELA VENKATA RAMYA , GANJI MOHANA PRIYA |
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IJRTI_205993
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| Published Paper Id: | IJRTI2509004 |
| Published In: | Volume 10 Issue 9, September-2025 |
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| Abstract: | Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, poses significant diagnostic and therapeutic challenges due to its heterogeneous manifestation across individuals. Electroencephalography (EEG) remains the primary diagnostic modality for capturing abnormal neural activity associated with seizures. However, manual EEG analysis is time-intensive and error-prone. This study presents a hybrid AI-based framework for epileptic seizure detection using both traditional machine learning (ML) and deep learning (DL) techniques. Specifically, the work investigates the performance of Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and a customized One-Dimensional Convolutional Neural Network (1D-CNN) on the UCI Epileptic Seizure Recognition Dataset, derived from the BONN dataset. The framework involves comprehensive preprocessing, feature extraction, and temporal pattern analysis from EEG signals. Experimental results demonstrate that the proposed 1D-CNN model achieved the highest classification accuracy of 98.78%, outperforming traditional ML methods. The study underscores the superiority of deep learning in capturing spatial-temporal features of EEG signals and affirms the clinical relevance of AI-assisted seizure detection. The findings advocate for the integration of automated systems in neurological diagnostics, promoting early intervention and improved patient care outcomes. |
| Keywords: | Epilepsy, EEG, Seizure Detection, Machine Learning, Deep Learning, XGBoost, LSTM, 1D-CNN, Biomedical Signal Processing, UCI Epileptic Dataset. |
| Cite Article: | "Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a41-a45, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509004.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2509004
Registration ID:205993
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier):
Page No: a41-a45 Country: HYDERABAD, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509004 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509004 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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