Seizure detection with reduced electroencephalogram channels: research trends and outlook

Abstract

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

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Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning

“…Investigating the correlation between different channels means that examining the role of the interaction among brain regions in modulating the epileptic seizure activity becomes a potentially crucial standard for the prediction of seizure onset [ 37 ]. The channel reduction in the seizure prediction system ensures the potential advantages of minimal energy consumption, reduction in overfitting rate, and increased time efficiency [ 38 , 39 ]. In this correlative channel reduction scheme, in the proposed approach, the significance of each channel’s contribution to the classification outcome is evaluated through the wrapper feature selection method [ 40 ].…”

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

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