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1. Forecasting epileptic seizures with wearable devices: A hybrid short‐ and long‐horizon pseudo‐prospective approach

   https://doi.org/10.1111/epi.18466  

   Nasseri, Mona; Stirling, Rachel E; Viana, Pedro F; Cui, Jie; Nurse, Ewan; Karoly, Philippa J; Kremen, Vaclav; Dümpelmann, Matthias; Worrell, Gregory A; Freestone, Dean R; et al (May 2025, Epilepsia)

   Abstract ObjectiveSeizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long‐term use. This study presents the first validation of a seizure‐forecasting system using ultra‐long‐term, non‐invasive wearable data. MethodsEleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist‐worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models—combining machine learning and cycle‐based methods—were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons. ResultsThe Seizure Warning System (SWS), designed for forecasting near‐term seizures, and the Seizure Risk System (SRS), designed for forecasting long‐term risk, both outperformed traditional models. In addition, the SRS reduced high‐risk time by 29% while increasing sensitivity by 11%. SignificanceThese improvements mark a significant advancement in making seizure forecasting more practical and effective. 

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2. Computing Functional Brain Connectivity in Neurological Disorders: Efficient Processing and Retrieval of Electrophysiological Signal Data

   Gershon, A; Devulapalli, P; Zonjy, B; Ghosh, K; Tatsuoka, C; Sahoo, SS (April 2019, AMIA Jt Summits Transl Sci Proc.)

   Brain functional network connectivity is an important measure for characterizing changes in a variety of neurological disorders, for example Alzheimer’s Disease, Parkinson Disease, and Epilepsy. Epilepsy is a serious neurological disorder affecting more than 50 million persons worldwide with severe impact on the quality of life of patients and their family members due to recurrent seizures. More than 30% of epilepsy patients are refractive to pharmacotherapy and are considered for resection to disrupt epilepsy seizure networks. However, 20-50% of these patients continue to have seizures after surgery. Therefore, there is a critical need to gain new insights into the characteristics of epilepsy seizure networks involving one of more brain regions and accurately delineate epileptogenic zone as a target for surgery. Although there is growing availability of large volume of high resolution stereotactic electroencephalogram (SEEG) data recorded from intracranial electrodes during presurgical evaluation of patients, there are significant informatics challenges associated with processing and analyzing this large signal dataset for characterizing epilepsy seizure networks. In this paper, we describe the development and application of a high-performance indexing structure for efficient retrieval of large-scale SEEG signal data to compute seizure network patterns corresponding to brain functional connectivity networks. This novel Neuro-Integrative Connectivity (NIC) search and retrieval method has been developed by extending the red-black tree index model together with an efficient lookup algorithm. We systematically perform a comparative evaluation of the proposed NIC index using de-identified SEEG data from a patient with temporal lobe epilepsy to retrieve segments of signal data corresponding to multiple seizure events and demonstrate the significant advantages of the NIC index as compared to existing methods. This new NIC Index enables faster computation of brain functional connectivity measures in epilepsy patients for large-scale network analysis and potentially provide new insights into the organization as well as evolution of seizure networks in epilepsy patients. 

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3. 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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4. Multimodal, Multiscale Insights into Hippocampal Seizures Enabled by Transparent, Graphene-Based Microelectrode Arrays

   https://doi.org/10.1523/eneuro.0386-21.2022  

   Mulcahey, Patrick J.; Chen, Yuzhang; Driscoll, Nicolette; Murphy, Brendan B.; Dickens, Olivia O.; Johnson, A. T.; Vitale, Flavia; Takano, Hajime (May 2022, eneuro)

   Hippocampal seizures are a defining feature of mesial temporal lobe epilepsy (MTLE). Area CA1 of the hippocampus is commonly implicated in the generation of seizures, which may occur because of the activity of endogenous cell populations or of inputs from other regions within the hippocampal formation. Simultaneously observing activity at the cellular and network scales in vivo remains challenging. Here, we present a novel technology for simultaneous electrophysiology and multicellular calcium imaging of CA1 pyramidal cells (PCs) in mice enabled by a transparent graphene-based microelectrode array (Gr MEA). We examine PC firing at seizure onset, oscillatory coupling, and the dynamics of the seizure traveling wave as seizures evolve. Finally, we couple features derived from both modalities to predict the speed of the traveling wave using bootstrap aggregated regression trees. Analysis of the most important features in the regression trees suggests a transition among states in the evolution of hippocampal seizures. 

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5. An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

   Aziz, A; Pham, N; Vora, N; Reynolds, C; Lehnen, J; Venkatesh, P; Yao, Z; Harvey, J; Vu, T; Ding, K; et al (January 2024, Arxiv)

   Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide. Fortunately, up to 70 percent of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing, computing, and communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer or mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3 percent accuracy by just using classical machine learning algorithms. 

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