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W2080966422 abstract "The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency ( t - f ) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t - f analysis of EEGs. Short-time Fourier transform and several t - f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t - f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t - f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented." @default.
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W2080966422 date "2009-09-01" @default.
W2080966422 modified "2023-10-17" @default.
W2080966422 title "Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis" @default.
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W2080966422 doi "https://doi.org/10.1109/titb.2009.2017939" @default.
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