Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients

@article{DAlessandro2003EpilepticSP,
  title={Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients},
  author={Maryann D'Alessandro and Rosana Esteller and George J. Vachtsevanos and Arthur Hinson and Javier R. Echauz and Brian Litt},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2003},
  volume={50},
  pages={603-615}
}
Epileptic seizure prediction has steadily evolved from its conception in the 1970s, to proof-of-principle experiments in the late 1980s and 1990s, to its current place as an area of vigorous, clinical and laboratory investigation. As a step toward practical implementation of this technology in humans, we present an individualized method for selecting electroencephalogram (EEG) features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of… 

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References

SHOWING 1-10 OF 90 REFERENCES
Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention.
  • K. Lehnertz, R. Andrzejak, C. Elger
  • Psychology
    Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society
  • 2001
TLDR
Findings indicate that it is indeed possible to detect a preseizure phase and the unequivocal definition of such a state with a sufficient length would enable investigations of basic mechanisms leading to seizure initiation in humans, and development of adequate seizure prevention strategies.
Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy--an overview.
  • K. Lehnertz
  • Psychology
    International journal of psychophysiology : official journal of the International Organization of Psychophysiology
  • 1999
The analysis of EEG texture content for seizure prediction
  • A. Petrosian, R. Homan
  • Computer Science
    Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 1994
TLDR
Although this study is preliminary and was carried out on the data obtained from one patient, the results showed feasibility of using signal texture information for distinguishing different abnormal patterns from EEG recordings.
Epileptic Seizures May Begin Hours in Advance of Clinical Onset A Report of Five Patients
Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings.
TLDR
A new method is described to analyze long-term non-stationarity in the EEG by a measure of dynamical similarity between different parts of the time series to track in real time spatio-temporal changes in brain dynamics several minutes prior to seizure.
Line length: an efficient feature for seizure onset detection
TLDR
A signal feature with low computational burden is presented as an efficient tool for seizure onset detection by evaluating 1,215 hours of intracranial EEG signal from 10 patients.
Wavelet-based texture analysis of EEG signal for prediction of epileptic seizure
TLDR
The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored and the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.
Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns
  • A. Petrosian
  • Computer Science
    Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems
  • 1995
TLDR
This paper analyzes Kolmogorov complexity and related characteristics of intracranial EEG recordings containing preictal, ictal and postictal segments and addresses the main issue of whether there exist a predictal phenomenon.
...
...