Publication

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

Publications associées (28)

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Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

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Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

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Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

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Disruption of layer-specific visual processing in a model of focal neocortical epilepsy

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The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and ...

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Epilepsy affects more than 40 million people worldwide, constituting one of the most debilitating disorders of the Central Nervous System (CNS). It results from an imbalance in the electrical activity of neurons, which is primarily mediated by calcium ions ...

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Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

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A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two- ...

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