Epileptic seizure detection: A nonlinear viewpoint

Päivinen, Niina; Lammi, Seppo; Pitkänen, Asla; Nissinen, Jari; Penttonen, Markku; Grönfors, Tapio

doi:10.1016/j.cmpb.2005.04.006

Cited by 100 publications

(59 citation statements)

References 11 publications

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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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“…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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“…For preprocessing the relevant features from the EEG [26,27] are selected during training with a forward feature selection algorithm. The EEG features used in this study were: a filter bank of Butterworth filters ranging from 1 to 30 Hz with a bandwidth of 2 Hz, a set of Daubechies 4 wavelet filters (level 2 to 6), the first derivative, the energy of the signal and the energy in the theta (4 to 8 Hz), alpha (8 to 12 Hz), beta (12 to 30 Hz) and gamma (>30 Hz) bands.…”

Section: Preprocessingmentioning

confidence: 99%

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Buteneers

Verstraeten

Mierlo

et al. 2011

Artificial Intelligence in Medicine

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Introduction: In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. Materials: The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452 hours from 23 GAERS and 982 hours from 15 kainate-induced temporal-lobe epilepsy rats. Methods: During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. Results: A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-theart technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Conclusion: Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.

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“…Absolute value of first and second derivative of neonatal sleep EEG were used for feature extraction in order to automatically detect the sleep stages [8]. First and second derivative of EEG were also used to extract time domain features for automatic seizure detection in [9]. Normalized absolute value of first or second derivative (depending on the patient) has been used to amplify the seizure part of the depth EEG with respect to the background [10], which facilitated automatic seizure detection (see also [11]).…”

mentioning

confidence: 99%

Automatic Seizure Detection in ECoG by Differential Operator and Windowed Variance

Majumdar

Vardhan

2011

IEEE Trans. Neural Syst. Rehabil. Eng.

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Epileptic seizure detection: A nonlinear viewpoint

Cited by 100 publications

References 11 publications

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

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

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Automatic Seizure Detection in ECoG by Differential Operator and Windowed Variance

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