An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals

@article{Tsiouris2015AnUM,
  title={An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals},
  author={Kostas M. Tsiouris and Spiros Konitsiotis and Sofia Markoula and Dimitrios D. Koutsouris and Antonis I. Sakellarios and Dimitrios I. Fotiadis},
  journal={2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)},
  year={2015},
  pages={1-4}
}
An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments… CONTINUE READING

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