Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

Li, Qiong; Gao, Jianbo; Zhang, Ziwen; Huang, Qi; Wu, Yuan; Xu, Bo

doi:10.3389/fphys.2020.00828

Cited by 15 publications

(12 citation statements)

References 74 publications

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“…Epileptic seizures are characterized by excessive neuronal synchronization and these hypersynchronous electrical potentials can be recorded with scalp electroencephalography (EEG). Due to the spontaneous nature of epileptic seizures, continuous and long-term EEG recordings of patients are often needed for their accurate diagnosis and precise treatment [ 1 , 2 ]. However, the visual inspection of EEG signals is laborious, time-consuming, and subject to rater-biases even by expert neurologists.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Kim

2022

JPM

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The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Kim

2022

JPM

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“…New computational and analytical approaches are needed to extract information from complex data, to infer transient interactions between dynamically changing systems, and to quantify global behavior at the organism level generated by networks of interactions that are function of time. In fact, in recent years, we have already witnessed the broad impact of introducing novel concepts and methods derived from modern statistical physics and network theory to biology and medicine, shifting the paradigm from reductionism to a new integrative framework essential to address fundamentally new problems in systems biology (Yao et al, 2019;Prats-Puig et al, 2020;Corkey and Deeney, 2020;Rizi et al, 2021;Barajas-Martínez et al, 2020), neuroscience (Castelluzzo et al, 2020;Pa¨eske et al, 2020;Fesce, 2020;Stramaglia et al, 2021), physiology (Podobnik et al, 2020;Zmazek et al, 2021), clinical medicine (Loscalzo and Barabasi, 2011;Delussi et al, 2020;Li et al, 2020;Liu et al, 2020;McNorgan et al, 2020;Tan et al, 2020;Haug et al, 2021;Liu et al, 2021) and even drug discovery (Hopkins, 2008). A central focus of research within this integrative framework is the interplay between structural connectivity and functional dependency, a key problem in neuroscience, brain research (Bullmore and Sporns, 2009;Gallos et al, 2012;Rothkegel and Lehnertz, 2014;Liu et al, 2015a;Bolton et al, 2020;Wang and Liu, 2020) and human physiology (Pereira-Ferrero et al, 2019;Lavanga et al, 2020;Barajas-Martínez et al, 2021;Gao et al, 2018;Balagué et al, 2020;Porta et al, 2017;Lioi et al, ...…”

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confidence: 99%

The New Field of Network Physiology: Building the Human Physiolome

Ivanov

2021

Front. Netw. Physiol.

100

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“…This was especially pronounced in case of large focal (hemispherical) bleeding compared with the contralateral side. (3) Epileptiform discharges were defined as spike, polyspikes, spike and slow wave complex, spike rhythm, sharp wave, and sharp and slow wave complex ( 24 ).…”

Section: Methodsmentioning

confidence: 99%

Neurophysiological Examinations as Adjunctive Tool to Imaging Techniques in Spontaneous Intracerebral Hemorrhage: IRONHEART Study

et al. 2021

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Introduction: Intracerebral hemorrhage (ICH) is a devastating disease, which may lead to severe disability or even death. Although many factors may influence the outcome, neurophysiological examinations might also play a role in its course. Our aim was to examine whether the findings of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) can predict the prognosis of these patients.Methods: Between June 1 2017 and June 15 2021, 116 consecutive patients with ICH were enrolled prospectively in our observational study. Clinical examinations and non-Contrast computed tomography (NCCT) scan were done on admission for ICH; follow-up NCCT scans were taken at 14 ± 2 days and at 3 months ± 7 days after stroke onset. EEG and TMS examinations were also carried out.Results: Of the patients in the study, 65.5% were male, and the mean age of the study population was 70 years. Most patients had a history of hypertension, 50.8% of whom had been untreated. In almost 20% of the cases, excessive hypertension was measured on admission, accompanied with >10 mmol/L blood glucose level, whereas their Glasgow Coma Scale was 12 on average. Presence of blood in the ventricles or subarachnoid space and high blood and perihematomal volumes meant poor prognosis. Pathological EEG was prognostic of a worse outcome. With TMS examination at 14 days, it might be possible to estimate outcome in a univariate model and the absence, or reduction of the amplitude of the motor evoked potentials was associated with poor prognosis.Conclusion: Together with the clinical symptoms, the volume of bleeding, perihematomal edema (or their combined volume), and neurophysiological examinations like EEG and TMS play an important role in the neurological outcome of patients with ICH. This might affect the patients' rehabilitation plans in the future, since with the help of the examinations the subset of patients with potential for recovery could be identified.

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Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective

Cited by 15 publications

References 74 publications

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

The New Field of Network Physiology: Building the Human Physiolome

Neurophysiological Examinations as Adjunctive Tool to Imaging Techniques in Spontaneous Intracerebral Hemorrhage: IRONHEART Study

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