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1. Epileptic Seizure Detection and Experimental Treatment: A Review

   https://doi.org/10.3389/fneur.2020.00701  

   Kim, Taeho; Nguyen, Phuc; Pham, Nhat; Bui, Nam; Truong, Hoang; Ha, Sangtae; Vu, Tam (July 2020, Frontiers in Neurology)
2. Efficient Epileptic Seizure Type Classification Using Hyperdimensional Computing

   Estiri, Seyedeh_Newsha; Daoud, Hisham; Najafi, M Hassan; Bayoumi, Magdy (October 2024, -)

   Precise seizure identification plays a vital role in understanding cortical connectivity and informing treatment decisions. Yet, the manual diagnostic methods for epileptic seizures are both labor-intensive and highly specialized. In this study, we propose a Hyperdimensional Computing (HDC) classifier for accurate and efficient multi-type seizure classification. Despite previous seizure analysis efforts using HDC being limited to binary detection (seizure or no seizure), our work breaks new ground by utilizing HDC to classify seizures into multiple distinct types. HDC offers significant advantages, such as lower memory requirements, a reduced hardware footprint for wearable devices, and decreased computational complexity. Due to these attributes, HDC can be an alternative to traditional machine learning methods, making it a practical and efficient solution, particularly in resource-limited scenarios or applications involving wearable devices. We evaluated the proposed technique on the latest version of TUH EEG Seizure Corpus (TUSZ) dataset and the evaluation result demonstrate noteworthy performance, achieving a weighted F1 score of 94.6%. This outcome is in line with, or even exceeds, the performance achieved by the state-ofthe-art traditional machine learning methods. 

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3. Benchmarking RRAM Crossbar Arrays for Epileptic Seizure Prediction

   https://doi.org/10.1109/MWSCAS60917.2024.10658668  

   Khan, Ahmedul; Varnosfaderani, Shiva Maleki; Alhawari, Mohammad; Tutuncuoglu, Gozde (August 2024, IEEE)

   This paper explores an energy-efficient resistive random access memory (RRAM) crossbar array framework for predicting epileptic seizures using the CHB-MIT electroencephalogram (EEG) dataset. RRAMs have significant potential for in-memory computing, offering a promising solution to overcome the limitations of the traditional Von Neumann architecture. By integrating a domain-specific feature extraction approach and evaluating the optimal RRAM hardware parameters using the NeuroSim+ benchmarking platform, we assess the performance of RRAM crossbars for predicting epileptic seizures. Our proposed workflow achieves accuracy levels above 80% despite the EEG data being quantized to 1-bit, highlighting the robustness and efficiency of our approach for epileptic seizure prediction 

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4. Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks

   https://doi.org/10.1109/CISS50987.2021.9400222  

   Currey, Danielle; Hsu, David; Ahmed, Raheel; Venkataraman, Archana; Craley, Jeff (March 2021, Conference on Information Sciences and Systems)
5. 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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