ArticleNeurosurgery2018
Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.
Article in Neurosurgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.Nature neuroscience · 2026Article
- Distance of bipolar re-referencing imparts nonlinear frequency-specific influences on intracranial recording signal measurements.Journal of neural engineering · 2025Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- Dissociating physiological ripples and epileptiform discharges with vision transformers.bioRxiv : the preprint server for biology · 2025Article
- SEEG in 2025: progress and pending challenges in stereotaxy methods, biomarkers and radiofrequency thermocoagulation.Current opinion in neurology · 2025Review
- Machine learning on interictal intracranial EEG predicts surgical outcome in drug resistant epilepsy.NPJ digital medicine · 2025Article
- The critical dynamics of hippocampal seizures.Nature communications · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Interictal epileptiform discharges contribute to word-finding difficulty in epilepsy through multiple cognitive mechanisms.Epilepsia · 2023Article
- Article
- A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.Nature communications · 2022Review
- Classifying epilepsy pragmatically: Past, present, and future.Journal of the neurological sciences · 2021Review
- Flexible, high-resolution thin-film electrodes for human and animal neural research.Journal of neural engineering · 2021Article
- Bidirectional propagation of low frequency oscillations over the human hippocampal surface.Nature communications · 2021Article
- Accuracy of omni-planar and surface casting of epileptiform activity for intracranial seizure localization.Epilepsia · 2021Article
- Under-sampling in epilepsy: Limitations of conventional EEG.Clinical neurophysiology practice · 2021Review
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
- Artificial Intelligence and Computational Approaches for Epilepsy.Journal of epilepsy research · 2020Review
- Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates.Science translational medicine · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundInterictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms.
objectiveTo develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition.
methodsWe decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions.
resultsThe receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges.
conclusionUnsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.