2010
DOI: 10.1097/wnp.0b013e3181e0a9b6
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Patient-Specific Early Seizure Detection From Scalp Electroencephalogram

Abstract: Objective Develop a method for automatic detection of seizures prior to or immediately after clinical onset using features derived from scalp EEG. Methods This detection method is patient-specific. It uses recurrent neural networks and a variety of input features. For each patient we trained and optimized the detection algorithm for two cases: 1) during the period immediately preceding seizure onset, and 2) during the period immediately following seizure onset. Continuous scalp EEG recordings (duration 15 – … Show more

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Cited by 97 publications
(71 citation statements)
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“…The adult brain also reliably expresses electrical activity patterns, enabling detection and prediction of specific states or events, such as drowsiness, seizure, and loss of focus [5], [6]. Development of a neurobehavioral user model that would enable the identification of deviations from typical patterns remains difficult due to the lack of a generic model upon which to base comparisons and build adaptive changes.…”
Section: Introductionmentioning
confidence: 99%
“…The adult brain also reliably expresses electrical activity patterns, enabling detection and prediction of specific states or events, such as drowsiness, seizure, and loss of focus [5], [6]. Development of a neurobehavioral user model that would enable the identification of deviations from typical patterns remains difficult due to the lack of a generic model upon which to base comparisons and build adaptive changes.…”
Section: Introductionmentioning
confidence: 99%
“…Seizure detection method should be able to automatically adapt it properties to tackle this condition. However most of the proposed methods rely on extensive pre-training process for their machine learning algorithm (e.g, using ANN [11,14,16] or SVM [10,12,[21][22]). Huge training data, both for normal EEG and EEG with seizures, are needed to obtain accurate seizure detector.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, several promising EEG signal processing and feature extraction methods for seizure detection are proposed [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. These methods could be further categorized into: timedomain (e.g, [10,11]), frequency-domain (e.g, using filter bank [12] and sign periodogram transform [13]), time-frequency domain (e.g, wavelet transform [14][15][16][17]), nonlinear methods (e.g, using various entropies [18][19]) or combination of them (e.g, [10,20]).…”
Section: Introductionmentioning
confidence: 99%
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