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1. Human seizures couple across spatial scales through travelling wave dynamics

   https://doi.org/10.1038/ncomms14896  

   Martinet, L-E; Fiddyment, G; Madsen, J R; Eskandar, E N; Truccolo, W; Eden, U T; Cash, S S; Kramer, M A (April 2017, Nature Communications)
2. Interneuron Desynchronization Precedes Seizures in a Mouse Model of Dravet Syndrome

   https://doi.org/10.1523/JNEUROSCI.2370-19.2020  

   Tran, Conny H.; Vaiana, Michael; Nakuci, Johan; Somarowthu, Ala; Goff, Kevin M.; Goldstein, Nitsan; Murthy, Priya; Muldoon, Sarah F.; Goldberg, Ethan M. (March 2020, The Journal of Neuroscience)
3. Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures

   https://doi.org/10.1038/s41598-020-65401-6  

   Bomela, Walter; Wang, Shuo; Chou, Chun-An; Li, Jr-Shin (December 2020, Scientific Reports)
4. Accuracy of Automated Machine Learning Software in Identifying EEGs with Prolonged Seizures

   Lin, Rebecca; Marquez, Destiny; Jacobson, Mercedes; Castaldi, Hannah; Buckman, Samuel; Shah, Vinit; Picone, Joseph (April 2020, Annual Meeting of the American Academy of Neurology (AAN))

   null (Ed.)

   Objective: To demonstrate that combining automatic processing of EEG data using high performance machine learning algorithms with manual review by expert annotators can quickly identify subjects with prolonged seizures. Background: Prolonged seizures are markers of seizure severity, risk of transformation into status epilepticus, and medical morbidity. Early recognition of prolonged seizures permits intervention and reduces morbidity. Design/Methods: We triaged the TUH EEG Corpus, an open source database of EEGs, by running a state-of-the-art hybrid LSTM-based deep learning system. Then, we postprocessed the output to identify high confidence hypotheses for seizures that were greater than three minutes in duration. Results: The triaging method selected 25 subjects for further review. 17 subjects had seizures; only 5 met criteria for seizures greater than 3 minutes. 11 subjects did not have a prior diagnosis of epilepsy. Among these, 63% had acute respiratory failure and 36% had cardiac arrest leading to seizures secondary to anoxic brain injury. 18 (72%) EEGs were obtained in long-term monitoring (LTM), 1 (4%) in the epilepsy monitoring unit (EMU), and 6 (24%) as a routine EEG (rEEG). 72.2% of seizures in LTM were identified correctly versus 66.7% in rEEGs. Of the 9 subjects who were deceased, 7 (78%) had been on LTM. The seizure detection algorithm misidentified seizures in 7 subjects (28%). A total of 22 (88%) subjects had some ictal pattern. Patterns mistaken for seizure activity included muscle artifact, generalized periodic discharges, generalized spike-and-wave, triphasic waves, and interestingly, an EEG recording captured during CPR. Conclusions: This hybrid approach, which combines state-of-the-art machine learning seizure detection software with human annotation, successfully identified prolonged seizures in 72% of subjects; 88% had ictal patterns. Prolonged seizures were more common in LTM subjects than the EMU and were associated with acute cardiac or pulmonary insult. 

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5. Multiple Sources of Fast Traveling Waves during Human Seizures: Resolving a Controversy

   https://doi.org/10.1523/JNEUROSCI.0338-22.2022  

   Schlafly, Emily D; Marshall, François A; Merricks, Edward M; Eden, Uri T; Cash, Sydney S; Schevon, Catherine A; Kramer, Mark A (September 2022, The Journal of Neuroscience)