ArticleEpilepsia open2019
An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy.
Article in Epilepsia open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Face-valid phenotypes in a mouse model of the most common mutation in EEF1A2-related neurodevelopmental disorder.Disease models & mechanisms · 2024Article
- Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice.Genes, brain, and behavior · 2024Article
- Abnormal patterns of sleep and waking behaviors are accompanied by neocortical oscillation disturbances in an Ank3 mouse model of epilepsy-bipolar disorder comorbidity.Translational psychiatry · 2023Article
- Isolation of the murine Glut1 deficient thalamocortical circuit: wavelet characterization and reverse glucose dependence of low and gamma frequency oscillations.Frontiers in neuroscience · 2023Article
- Machine learning approaches reveal subtle differences in breathing and sleep fragmentation inJournal of neurophysiology · 2021Article
- Sleep-wake characteristics in a mouse model of severe traumatic brain injury: Relation to posttraumatic epilepsy.Epilepsia open · 2021Article
- Man and the machine rise to the spike-wave. Commentary on "An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy."Epilepsia open · 2020Article
- An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy.Epilepsia open · 2019Article
- Home-cage monitoring ascertains signatures of ictal and interictal behavior in mouse models of generalized seizures.PloS one · 2019Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectiveManual 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.
methodsWe 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.
resultsThe 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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