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

   https://doi.org/10.1145/3703164  

   Aziz, Abdul; Pham, Nhat; Vora, Neel; Reynolds, Cody; Lehnen, Jaime; Venkatesh, Pooja; Yao, Zhuoran; Harvey, Jay; Vu, Tam; Ding, Kan; et al (January 2025, ACM Transactions on Computing for Healthcare)

   Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% 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 current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developedEarSD, a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring.EarSDis an integrated custom-builtsensing-computing-communicationear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby.EarSD’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality. 

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2. Mapping epileptic directional brain networks using intracranial EEG data

   https://doi.org/10.1093/biostatistics/kxz056  

   Li, Huazhang; Wang, Yaotian; Tanabe, Seiji; Sun, Yinge; Yan, Guofen; Quigg, Mark S.; Zhang, Tingting (December 2019, Biostatistics)

   Summary The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. As such, effective epilepsy diagnosis and treatment require accurate identification of directional connections among regions, i.e., mapping of epileptic patients’ brain networks. This article aims to understand the epileptic brain network using intracranial electroencephalographic data—recordings of epileptic patients’ brain activities in many regions. The most popular models for directional connectivity use ordinary differential equations (ODE). However, ODE models are sensitive to data noise and computationally costly. To address these issues, we propose a high-dimensional state-space multivariate autoregression (SSMAR) model for the brain’s directional connectivity. Different from standard multivariate autoregression and SSMAR models, the proposed SSMAR features a cluster structure, where the brain network consists of several clusters of densely connected brain regions. We develop an expectation–maximization algorithm to estimate the proposed model and use it to map the interregional networks of epileptic patients in different seizure stages. Our method reveals the evolution of brain networks during seizure development. 

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3. 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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4. 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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5. Mitigating Epilepsy by Stabilizing Linear Fractional-Order Systems

   https://doi.org/10.23919/ACC55779.2023.10156404  

   Emily A. Reed, Guilherme Ramos (May 2023, 2023 American Control Conference (ACC))

   Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems. 

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