Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis

Sitnikova, Evgenia; Hramov, Alexander E.; Koronovsky, A. A.; Luijtelaar, Gilles van

doi:10.1016/j.jneumeth.2009.04.006

Cited by 136 publications

(91 citation statements)

References 43 publications

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“…Comparison between the manual and that investigator's for SWDs in rodents [11][12][13] . In these three publications, the analyses were based on transforming the EEG from the time domain to the frequency domain to quantify changes.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…2009 was based on spectral and variant analysis and that of Sitnikova et al 2009 on spectral and wavelet analysis [12,13] .…”

Section: Discussionmentioning

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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“…To perform sleep classification, the AQSSA-SE method and a wavelet-based approach, which is a common method for analysis of EEG subbands [27], [28], were employed on sleep EEG to extract frequency bands delta, theta, alpha, sigma, and beta. Morlet wavelet transform which was used for comparison in this work, is an established approach for providing high time-frequency resolution in sleep signal analysis especially for detection of sleep abnormalities and spindles [28].…”

Section: Application To Sleep Eegmentioning

confidence: 99%

“…Morlet wavelet transform which was used for comparison in this work, is an established approach for providing high time-frequency resolution in sleep signal analysis especially for detection of sleep abnormalities and spindles [28]. As wavelet works with single channel data, it was applied on four channels (F4-A1, C4-A1, P4-A1, and O2-A1) individually and results were averaged to generate the final output.…”

Section: Application To Sleep Eegmentioning

confidence: 99%

“…However, in contrast to [8], we consider fivestage classification. The basis for sleep EEG analysis lies in the estimation of the brain rhythms such as delta (less than 4 Hz), theta (4-7 Hz), alpha (8-12 Hz), sigma (11)(12)(13)(14)(15)(16), and beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) from the EEG, that exhibit different characteristics in various stages of sleep, especially in the context of non-rapid eye movement (NREM) [1], [19]. NREM sleep is divided into three stages, namely N1, N2 and N3.…”

Section: Introductionmentioning

confidence: 99%

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Quaternion Singular Spectrum Analysis of Electroencephalogram With Application in Sleep Analysis

Enshaeifar

Kouchaki

Took

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-A novel quaternion-valued singular spectrum analysis (SSA) is introduced for multichannel analysis of electroencephalogram (EEG). The analysis of EEG typically requires the decomposition of data channels into meaningful components despite the notoriously noisy nature of EEG -which is the aim of SSA. However, the singular value decomposition involved in SSA implies the strict orthogonality of the decomposed components, which may not reflect accurately the sources which exhibit similar neural activities. To allow for the modelling of such co-channel coupling, the quaternion domain is considered for the first time to formulate the SSA using the augmented statistics. As an application, we demonstrate how the augmented quaternionvalued SSA (AQSSA) can be used to extract the sources, even at a signal-to-noise ratio as low as -10 dB. To illustrate the usefulness of our quaternion-valued SSA in a rehabilitation setting, we employ the proposed SSA for sleep analysis to extract statistical descriptors for five-stage classification (Awake, N1, N2, N3 and REM). The level of agreement using these descriptors was 74% as quantified by the Cohen's kappa.

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Automatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks

Tomanik

Betting

Campanharo

2019

Advances in Computational Intelligence

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Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis

Cited by 136 publications

References 43 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

Quaternion Singular Spectrum Analysis of Electroencephalogram With Application in Sleep Analysis

Automatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks

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