When working with seizure detection, the process of identifying epileptic events using data and devices. Also known as epilepsy monitoring, it relies heavily on electroencephalography (EEG), wearable sensors, and machine learning algorithms to interpret brain activity in real time. Seizure detection encompasses data collection, signal processing, and alert generation, creating a feedback loop that helps patients and clinicians act quickly. The three pillars—EEG, wearables, and AI—form a connected ecosystem where each piece amplifies the others' value.
EEG monitoring provides the raw electrical signatures that define a seizure, capturing spikes, waves, and rhythmic patterns that are invisible to the naked eye. Wearable sensors extend this capability beyond the clinic, attaching to the scalp or body to record motion, heart rate, and skin conductance, which together enrich the EEG signal with context. Machine learning algorithms then sift through millions of data points, spotting subtle pre‑ictal changes that humans might miss and generating alerts with seconds to spare. This triad enables continuous, home‑based monitoring, reduces hospital visits, and supports personalized treatment plans. Researchers also integrate cloud platforms for remote analysis, so clinicians can review episodes in near‑real time, adjusting medication dosage or recommending lifestyle changes based on objective evidence.
Below you’ll find a curated set of articles that dive deeper into each of these areas—clinical studies on EEG‑based detection, reviews of the latest wearable devices, and practical guides on implementing machine‑learning pipelines for seizure prediction. Whether you’re a patient looking for the best monitoring option or a clinician seeking evidence‑based tools, the collection offers actionable insights to help you navigate the evolving landscape of seizure detection.
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