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Research Paper | Health and Medical Sciences | Volume 15 Issue 5, May 2026 | Pages: 1 - 6 | India
Automated Detection of Epileptic Seizures Using EEG Signals and Gradient Boosting Approach
Abstract: Epilepsy is a neurological disorder in which seizures recur due to abnormal electrical activity in the brain. It is a critical problem affecting millions of people worldwide, which impacts their quality of life. Electro-encephalography (EEG) is a popular approach in which brain signals can be recorded, and abnormal signals can be identified. However, analyzing EEG signals manually is a tedious job, which requires expertise from neurologists. In addition, analyzing EEG signals for a prolonged period results in a high volume of signals, which makes it difficult to analyze manually. In order to mitigate the challenges in analyzing EEG signals, many researchers have proposed machine learning-based epilepsy detection systems. Machine learning algorithms can analyze EEG signals, identify meaningful features, and classify them as seizure and non-seizure signals. In this research, an automated system to detect seizures by using EEG signals is presented. The system uses EEG signals collected in the Bonn University data set. The system is based on preprocessing EEG signals, feature extraction, and classification by using the Extreme Gradient Boosting algorithm. Nineteen features such as statistical features and frequency features, including mean, standard deviation, entropy, signal energy, and Hjorth features, are used to represent significant characteristics of EEG signals. The features extracted by the system help in training the Extreme Gradient Boosting classifier to classify seizure and normal EEG signals. The results show that the system can accurately classify EEG signals and detect seizure patterns. This system can help neurologists in diagnosing patients more efficiently.
Keywords: EEG signals, epilepsy detection, seizure classification, machine learning, feature extraction
How to Cite?: Vadavalli Bhavani, "Automated Detection of Epileptic Seizures Using EEG Signals and Gradient Boosting Approach", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 1-6, https://www.ijsr.net/getabstract.php?paperid=SR26430095205, DOI: https://dx.dx.doi.org/10.21275/SR26430095205