An algorithm for on-line detection of high frequency oscillations related to epilepsy

López-Cuevas, Armando; Castillo–Toledo, B.; Medina‐Ceja, Laura; Ventura-Mejía, Consuelo; Pardo-Peña, Kenia

doi:10.1016/j.cmpb.2013.01.014

Cited by 32 publications

(22 citation statements)

References 32 publications

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“…HFO detectors can be subdivided in time-based (Staba et al , 2002, Gardner et al , 2007, Worrell et al , 2008, Blanco et al , 2010, Dümpelmann et al , 2012, López-Cuevas et al , 2013 and time-frequency algorithms (Birot et al , 2013, Burnos et al , 2014, Burnos et al , 2016b. In the second step of our detector, we analyze individual blobs in time-frequency domain by disentangling HFOs from broadband artifacts by the duration of the event in the instantaneous power spectra.…”

Section: Comparison To the Literaturementioning

confidence: 99%

“…Several automatic HFO detectors have been developed by different research groups (Staba et al , 2002, Gardner et al , 2007, Worrell et al , 2008, Blanco et al , 2010, Dümpelmann et al , 2012, Birot et al , 2013, López-Cuevas et al , 2013, Burnos et al , 2014, Burnos et al , 2016b. The general implementation follows a two-stage procedure: a first step aims to identify a reliable threshold that we use to isolate events of interest (EoI), and a second step recognizing HFOs from spurious EoI, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

Fedele

Klooster

Burnos

et al. 2016

Clinical Neurophysiology

110

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Section: Comparison To the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

Fedele

Klooster

Burnos

et al. 2016

Clinical Neurophysiology

110

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“…The basal electrical activity of each control group was analyzed, and the amplitude and frequency averaged over a 15 min recording period. In contrast, once recorded, EEG traces from all recordings of experimental animals were converted to MATLAB readable files to process and identify the FRs using an algorithm designed specifically for this purpose [34–36]. Accordingly, each of the signals selected was passed through a 100–650 Hz band-pass filter using the Hamming method with 60 coefficients.…”

Section: Methodsmentioning

confidence: 99%

Decreased Fast Ripples in the Hippocampus of Rats with Spontaneous Recurrent Seizures Treated with Carbenoxolone and Quinine

Ventura-Mejía

Medina‐Ceja

2014

BioMed Research International

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Background. In models of temporal lobe epilepsy and in patients with this pathology, high frequency oscillations called fast ripples (FRs, 250–600 Hz) can be observed. FRs are considered potential biomarkers for epilepsy and, in the light of many in vitro and in silico studies, we thought that electrical synapses mediated by gap junctions might possibly modulate FRs in vivo. Methods. Animals with spontaneous recurrent seizures induced by pilocarpine administration were implanted with movable microelectrodes in the right anterior and posterior hippocampus to evaluate the effects of gap junction blockers administered in the entorhinal cortex. The effects of carbenoxolone (50 nmoles) and quinine (35 pmoles) on the mean number of spontaneous FR events (occurrence of FRs), as well as on the mean number of oscillation cycles per FR event and their frequency, were assessed using a specific algorithm to analyze FRs in intracranial EEG recordings. Results. We found that these gap junction blockers decreased the mean number of FRs and the mean number of oscillation cycles per FR event in the hippocampus, both during and at different times after carbenoxolone and quinine administration. Conclusion. These data suggest that FRs may be modulated by gap junctions, although additional experiments in vivo will be necessary to determine the precise role of gap junctions in this pathological activity associated with epileptogenesis.

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“…Niknazar et al (2013) propose a unified thresholding approach using several features from time domain, frequency domain and non-linear properties, able to discriminate during seizure and after seizure states. López-Cuevas et al (2013) propose an algorithm based on artificial neural networks for automatic detection of high frequency oscillations related to epilepsy.…”

Section: Introductionmentioning

confidence: 99%

Seizure states identification in experimental epilepsy using gabor atom analysis

Sotelo

Guijarro

Trujillo

2015

Journal of Neuroscience Methods

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Background:Epileptic seizures evolve through several states, and in the process the brain signals may change dramatically. Signals from different states share similar features, making it difficult to distinguish them from a time series; the goal of this work is to build a classifier capable of identifying seizure states based on time-frequency features taken from short signal segments. Methods:There are different amounts of frequency components within each Time--Frequency window for each seizure state, referred to as the Gabor atom density. Taking short signal segments from the different states and decomposing them into their atoms, the present paper suggests that is possible to identify each seizure state based on the Gabor atom density. The brain * Corresponding author Email addresses: asotelo@tectijuana.mx (Arturo Sotelo), eguijarro@eln.upv.es (Enrique D. Guijarro), leonardo.trujillo@tectijuana.edu.mx (Leonardo Trujillo) Preprint submittedNovember 12, 2014 signals used in this work were taken form a database of intracranial recorded seizures from the Kindling model. Results:The findings suggest that short signal segments have enough information to be used to derive a classifier able to identify the seizure states with reasonable confidence, particularly when used with seizures from the same subject.Achieving average sensitivity values between 0.82 and 0.97, and area under the curve values between 0.5 and 0.9. Conclusions:The experimental results suggest that seizure states can be revealed by the Gabor atom density; and combining this feature with the epoch's energy produces an improved classifier. These results are comparable with the recently published on state identification. In addition, considering that the order of seizure states is unlikely to change, these results are promising for automatic seizure state classification.

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An algorithm for on-line detection of high frequency oscillations related to epilepsy

Cited by 32 publications

References 32 publications

Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

Decreased Fast Ripples in the Hippocampus of Rats with Spontaneous Recurrent Seizures Treated with Carbenoxolone and Quinine

Seizure states identification in experimental epilepsy using gabor atom analysis

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