An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings

Hopfengärtner, R.; Kerling, Frank; Bauer, Veronika; Stefan, Hermann

doi:10.1016/j.clinph.2007.07.017

Cited by 72 publications

(47 citation statements)

References 19 publications

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“…For EEG data average sensitivity of 80%, selectivity of <1.56 FPH, and delay of 10 s were obtained. These results are comparable to results of others obtained on large scalp EEG datasets: in [18] sensitivity of 90.9%, selectivity of 0.29 FPH, and delays of 10-44 s were reported. In [2] sensitivity of >96%, selectivity of <0.5 FPH, and average delay of 1.6 s were obtained.…”

Section: Human Datasupporting

confidence: 80%

“…Moreover, on data from human epilepsy patients this detector not only showed the results on par or better compared to the results of others [2,18,19] but its performance on tEEG data was directly compared to performance on simultaneously recorded conventional EEG data once again confirming the advantage of TCREs over conventional disc electrodes [11]. Recording HFOs with scalp EEG is very difficult [20] but we recently showed on data from animal model [12] and human patients with epilepsy [1] that they can be recorded with tEEG.…”

Section: Discussionmentioning

confidence: 82%

“…A non patient-specific model was used through k-fold cross-validation with each fold representing all the available data from a single patient to ensure direct comparison with results of others [2,18,19]. Sensitivity (correct detection rate), selectivity (false positive detections per hour, FPH), and detection delay were analyzed on EEG and tEEG data.…”

Section: Human Datamentioning

confidence: 99%

“…EEF based seizure onset detection has been validated on tEEG data both from the animal model [10] and human epilepsy patients [11]. The latter study included a comparison to the EEF performance on simultaneously recorded conventional EEG data [11] as well as a comparison to the EEG based results of others [2,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in High-Frequency Oscillations and Seizure Onset Detection Using Laplacian Electroencephalography via Tripolar Concentric Ring Electrodes

Makeyev

Musngi

Lee

et al. 2017

The 4th International Electronic Conference on Sensors and Applications

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Laplacian electroencephalogram signal from novel and noninvasive tripolar concentric ring electrodes has been demonstrated to have superior performance compared to the electroencephalogram from conventional disc electrodes due to its unique capabilities which allow automatic attenuation of common movement and muscle artifacts in applications including braincomputer interface, seizure onset detection, and detection of high-frequency oscillations and seizure onset zones. This review paper covers the recent advances in the fields of high-frequency oscillations and seizure onset detection based on tripolar Laplacian electroencephalography in animal models and human data to improve the diagnostic yield of electroencephalography for epilepsy. Progression of methodologies utilized including integration of multiple sensors using exponentially embedded families and results obtained including a comparison to the results of others as well as to the performance of the same detector on simultaneously recorded electroencephalogram via conventional disc electrodes is discussed in detail. Specific advantages of using this particular sensor for these particular applications are highlighted. Promising directions for the future work and an overview of currently ongoing research are discussed along with the potential of combining the two detectors and using automatically detected high-frequency oscillations that have been shown to be indicative of early seizure development as auxiliary features for the seizure onset detection.

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Section: Human Datasupporting

confidence: 80%

Section: Discussionmentioning

confidence: 82%

Section: Human Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recent Advances in High-Frequency Oscillations and Seizure Onset Detection Using Laplacian Electroencephalography via Tripolar Concentric Ring Electrodes

Makeyev

Musngi

Lee

et al. 2017

The 4th International Electronic Conference on Sensors and Applications

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“…In this study, other linear and non-linear parameters are also compared as well. Algorithms using power spectrum to automatically detect seizures are described in [Murro 1991] [ Hopfengartner et al 2007] and [Friedman et al 2008], to cite a few.…”

Section: Power Spectrum and Relevant Frequency Bandsmentioning

confidence: 99%

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

Ayala¹

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This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface.A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function's slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes.The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth's parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty.It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

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Conducting Polymer Electrodes for Electroencephalography

Leleux

Badier

Rivnay

et al. 2013

Adv Healthcare Materials

102

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An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings

Cited by 72 publications

References 19 publications

Recent Advances in High-Frequency Oscillations and Seizure Onset Detection Using Laplacian Electroencephalography via Tripolar Concentric Ring Electrodes

Recent Advances in High-Frequency Oscillations and Seizure Onset Detection Using Laplacian Electroencephalography via Tripolar Concentric Ring Electrodes

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

Conducting Polymer Electrodes for Electroencephalography

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