ArticleJournal of neural engineering2017
Behavioral state classification in epileptic brain using intracranial electrophysiology.
Article in Journal of neural engineering, 2017. 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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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
- Post-Ictal Sleep Changes in Human Focal Epilepsy.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- OpenSpindleNet: An open-source deep learning network for reliable sleep spindle detection in scalp and intracranial EEG.Computers in biology and medicine · 2025Article
- Microglial TREM2 promotes phagocytic clearance of damaged neurons after status epilepticus.Brain, behavior, and immunity · 2025Article
- Modulating limbic circuits in temporal lobe epilepsy: impacts on seizures, memory, mood and sleep.Brain communications · 2025Article
- Microglia enhance post-anesthesia neuronal activity by shielding inhibitory synapses.Nature neuroscience · 2024Article
- Non-rapid eye movement sleep slow-wave activity features are associated with amyloid accumulation in older adults with obstructive sleep apnoea.Brain communications · 2024Article
- Impedance Rhythms in Human Limbic System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Automated sleep classification with chronic neural implants in freely behaving canines.Journal of neural engineering · 2023Article
- Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep.Life (Basel, Switzerland) · 2023Article
- Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans.Journal of neural engineering · 2022Article
- Intravenous and Intramuscular Allopregnanolone for Early Treatment of Status Epilepticus: Pharmacokinetics, Pharmacodynamics, and Safety in Dogs.The Journal of pharmacology and experimental therapeutics · 2022Article
- Article
- Big data in status epilepticus.Epilepsy & behavior : E&B · 2019Review
- Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.Scientific reports · 2019Article
- Multi-feature localization of epileptic foci from interictal, intracranial EEG.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2019Article
- Slowing less than 1 Hz is decreased near the seizure onset zone.Scientific reports · 2019Article
- Cloud computing for seizure detection in implanted neural devices.Journal of neural engineering · 2019Article
- Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
- A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders.IEEE transactions on biomedical circuits and systems · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveAutomated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. MAIN
resultsClassification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. SIGNIFICANCE: Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.