A Novelty Detection Approach to Seizure Analysis from Intracranial EEG

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dc.contributor.author Gardner, Andrew Britton en_US
dc.date.accessioned 2005-03-03T22:07:05Z
dc.date.available 2005-03-03T22:07:05Z
dc.date.issued 2004-04-12 en_US
dc.identifier.uri http://hdl.handle.net/1853/5225
dc.description.abstract A Novelty Detection Approach to Seizure Analysis from Intracranial EEG Andrew B. Gardner 146 pages Directed by Dr. George Vachtsevanos and Dr. Brian Litt A framework for support vector machine classification of time series events is proposed and applied to analyze physiological signals recorded from epileptic patients. In contrast to previous works, this research formulates seizure analysis as a novelty detection problem which allows seizure detection and prediction to be treated uniformly, in a way that is capable of accommodating multichannel and/or multimodal measurements. Theoretical properties of the support vector machine algorithm employed provide a straightforward means for controlling the false alarm rate of the detector. The resulting novelty detection system was evaluated both offline and online on a corpus of 1077 hours of intracranial electroencephalogram (IEEG) recordings from 12 patients diagnosed with medically resistant temporal lobe epilepsy during evaluation for epilepsy surgery. These patients collectively had 118 seizures during the recording period. The performance of the novelty detection framework was assessed with an emphasis on four key metrics: (1) sensitivity (probability of correct detection), (2) mean detection latency, (3) early-detection fraction (prediction or detection of seizure prior to electrographic onset), and (4) false positive rate. Both the offline and online novelty detectors achieved state-of-the-art seizure detection performance. In particular, the online detector achieved 97.85% sensitivity, -13.3 second latency, and 40% early-detection fraction at an average of 1.74 false positive predictions per hour (Fph). These results demonstrate that a novelty detection approach is not only feasible for seizure analysis, but it improves upon the state-of-the-art as an effective, robust technique. Additionally, an extension of the basic novelty detection framework demonstrated its use as a simple, effective tool for examining the spread of seizure onsets. This may be useful for automatically identifying seizure focus channels in patients with focal epilepsies. It is anticipated that this research will aid in localizing seizure onsets, and provide more efficient algorithms for use in a real device. en_US
dc.format.extent 1308528 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject 1-class svm en_US
dc.subject Novelty detection
dc.subject Seizure analysis
dc.subject Seizure detection
dc.subject Support vector machine
dc.title A Novelty Detection Approach to Seizure Analysis from Intracranial EEG en_US
dc.type Dissertation en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Electrical and Computer Engineering en_US
dc.description.advisor Committee Chair: Lanterman, Aaron; Committee Member: Butera, Robert; Committee Member: Esteller, Rosana; Committee Member: Koblasz, Arthur; Committee Member: Litt, Brian; Committee Member: Vachtsevanos, George en_US


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