An algorithm for real-time detection of spike-wave discharges in rodents

Ovchinnikov, A. A.; Lüttjohann, Annika; Hramov, Alexander E.; Luijtelaar, Gilles van

doi:10.1016/j.jneumeth.2010.09.017

Cited by 87 publications

(56 citation statements)

References 27 publications

(34 reference statements)

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“…No performance evaluation of the method has been reported in their original paper. Ovchinnikov et al (2010) developed and evaluated an on-line SWD detection system based on a single frontal ECoG channel in WAG/Rij rats, sample rate 500 Hz. They used the energy in the 30-80 Hz characteristic frequency band during SWDs as was proposed by Sitnikova et al (2009).…”

Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

“…The authors also used the CWT approach and now an to the SWD pattern adapted mother wavelet was chosen in two frequency bands: the 10-11 and 30-80 Hz. The first is the frequency band of the onset of SWD activity, the latter band has been proposed by Ovchinnikov et al (2010). The reason for their choice is that sleep spindles and intermediate state on the one hand and SWDs on the other differ in the absence-presence of these high frequencies and in this way the false detection rate might be reduced.…”

Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

“…Analyzing the results of the developing of the different mathematical methods and software for on-line ECoG application and perhaps diagnostics, we conclude that nowadays the computing power of personal computers or laptops and the effectiveness of developed modern algorithms of data processing allows the analysis of multi-channel EEG in real time. For example, Ovchinnikov et al (2010Ovchinnikov et al ( , 2011 noted that the developed wavelet-based method of on-line SWD detection can be effectively used for simultaneous processing of about 20 channels on a single middle-power standard PC (hardware processor Intel Core 2 Quad Q6600 Kentsfield (2400MHz, LGA775, L2 8192Kb, 1066MHz), RAM 2GB). In a recent monography (Hramov et al, 2015) the fast algorithms of wavelet transform are described and discussed with an eye to the application of on-line EEG/MEG digital processing.…”

Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

Luijtelaar

Lüttjohann

Макаров

et al. 2016

Journal of Neuroscience Methods

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Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

Section: Detection Of Swds: Off Line Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

Luijtelaar

Lüttjohann

Макаров

et al. 2016

Journal of Neuroscience Methods

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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%

“…The methodology reported by Ovchinnikov et al 2010, only detected the onset of SWDs (evaluating the number of leading edges without taking the length of the detector event into consideration) and was based on wavelet analysis [11] . The methodology reported by Van Hese et al…”

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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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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An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

2019

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Summary Objective Manual detection of spike‐wave discharges (SWDs) from electroencephalography (EEG) records is time intensive, costly, and subject to inconsistencies/biases. In addition, manual scoring often omits information on SWD confidence/intensity, which may be important for the investigation of mechanistic‐based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of preselected events to establish a confidence‐based, continuous‐valued scoring. Methods We develop a support vector machine (SVM)–based algorithm for the automated detection of SWDs in the γ2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency‐ and amplitude‐based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained an SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60, 24‐hour EEG records. We provide a detailed assessment of intra‐ and interrater scoring that demonstrates advantages of automated scoring. Results The algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep. Significance Although there are automated and semi‐automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand “ground truth” in SWD detection (ie, the most reliable features agreed upon by humans that also correlate with objective physiologic measures).

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An algorithm for real-time detection of spike-wave discharges in rodents

Cited by 87 publications

References 27 publications

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

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

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

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