A comparison of quantitative EEG features for neonatal seizure detection

Greene, Barry R.; Faul, Stephen; Marnane, William P.; Lightbody, Gordon; Korotchikova, Irina; Boylan, Geraldine B.

doi:10.1016/j.clinph.2008.02.001

Cited by 199 publications

(134 citation statements)

References 42 publications

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“…Line length has been proposed as a potential feature for seizure detection in both adults et al 2003) and neonates (Greene et al 2008). For each EEG epoch x of length N, line length L was computed using (1)…”

Section: Line Lengthmentioning

confidence: 99%

Improved patient specific seizure detection during pre-surgical evaluation

Chua

Patel

Fitzsimons

et al. 2011

Clinical Neurophysiology

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Publication informationClinical Neurophysiology, 122 (4): 672-679Publisher Elsevier Item record/more information http://hdl.handle.net/10197/7034 Publisher's statementThis is the author's version of a work that was accepted for publication in Clinical Neurophysiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Clinical Neurophysiology (VOL 122, ISSUE 4, (2011) AbstractObjective: There is considerable interest in improved off-line automated seizure detection methods that will decrease the workload of EEG monitoring units. Also, subject-specific approaches have been demonstrated to perform better than subject-independent ones. However, for pre-surgical diagnostics, the traditional method of obtaining a priori data to train subject-specific classifiers is not practical. We present an alternative method that works by adapting the threshold of a subject-independent to a specific subject based on feedback from the user. Methods:A subject-independent quadratic discriminant classifier incorporating modified features based partially on the Gotman algorithm was first built. It was then used to derive subject-specific classifiers by determining subject-specific posterior probability thresholds via user interaction. The two schemes were tested on 529 hours of intracranial EEG containing 63 seizures from 15 subjects undergoing pre-surgical evaluation. To provide comparison, the standard Gotman algorithm was implemented and optimised for this dataset by tuning the detection thresholds.Results: Compared to the tuned Gotman algorithm, the subject-independent scheme reduced the false positive rate by 51 % (0.23 h -1 to 0.11 h -1 ) while increasing sensitivity from 53 % to 62 %. The subject-specific scheme further improved sensitivity to 78 %, but with a small increase in false positive rate to 0.18 h -1 ). Conclusions:The results suggest that a subject-independent classifier scheme with modified features is useful for reducing false positive rate, while subject adaptation further enhances performance by improving sensitivity. The results also suggest that 4 the proposed subject-adapted classifier scheme approximate the performance of the subject-specific Gotman algorithm. Significance:The proposed method could potentially help increase productivity of offline EEG analysis. The approach could also be generalised to enhance the performance of other subject independent algorithms. 5

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Section: Line Lengthmentioning

confidence: 99%

Improved patient specific seizure detection during pre-surgical evaluation

Chua

Patel

Fitzsimons

et al. 2011

Clinical Neurophysiology

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“…Many groups have published methods that automatically detect seizures, but they are predominantly designed for studies in humans [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy

et al. 2015

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The aim of this prospective blinded study was to evaluate an automated algorithm for spike-andwave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

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“…Recently, the detection of newborn EEG seizure has been automated to assist the neurophysiologist when diagnosing long recordings of EEG, [1], [9], [11], [12], [19], [27]. Current techniques, although constantly improving, lack the necessary accuracy that is required for clinical implementation.…”

Section: Secondsmentioning

confidence: 99%