Automated seizure detection using Short-Time Fourier Transform, theta band extraction, and LightGBM or CatBoost classifiers achieved an accuracy of 98.33%.
Does the use of STFT with theta band extraction and LightGBM/CatBoost classifiers accurately detect epileptical seizures in EEG signals?
A machine learning approach using STFT, theta band extraction, and LightGBM/CatBoost classifiers achieved 98.33% accuracy for automated seizure detection in EEG signals.
The EEG signal is made up of numerous frequency bands that characterize human behaviours like emotion, attentiveness, and sleep status, among others. In order to detect epileptical seizures, categorization based on discrete EEG segments is required. The performance of the theta band in an EEG signal is analyzed with the Short-Time Fourier Transform (STFT). It also analyses different categorization methodologies, demonstrating that some classification algorithms achieve extremely high accuracy. The analysis was done in stages, with STFT, theta frequency band extraction, statistical feature extraction, and then classification using LightGBM and Catboost classifier at the end. STFT is used in this study to extract statistical properties from 2-dimensional data and classify epilepsy in the low frequency range. The proposed LightGBM and CatBoost classifier got 98.33% accuracy.
Jiwani et al. (Wed,) conducted a other in Epileptical seizures. LightGBM and CatBoost classifiers with STFT and theta band extraction was evaluated on Classification accuracy. Automated seizure detection using Short-Time Fourier Transform, theta band extraction, and LightGBM or CatBoost classifiers achieved an accuracy of 98.33%.