EEG seizure detection is a method for identifying abnormal electrical activity in the brain indicative of epileptic seizures using electroencephalography.Electroencephalogram (EEG) records the total electrical activity of brain neurons using electrodes on the scalp. An epileptic seizure manifests as abnormal synchronous discharges, which on EEG look like sharp bursts (spikes) and slow waves. Detection algorithms analyze the multichannel signal, extracting characteristic patterns whose frequency and shape differ from background activity.
The first automatic seizure detectors appeared in the 1970s and were based on simple threshold rules for amplitude and duration. In the 1990s, frequency analysis methods and neural networks began to be used. With the growth of computing power and the availability of large labeled databases (e.g., the Temple University Hospital project) in the 2010s, there was a leap to deep learning, allowing performance on par with expert clinicians.
The system receives a multichannel EEG signal (usually 16-64 leads), digitized at 200-500 Hz. Preprocessing algorithms remove artifacts (filtering, independent component analysis). Then either predefined features are extracted (e.g., power spectral density in delta, theta, alpha, beta, gamma bands, fractal dimension, synchronization), or the signal is directly fed into a neural network. The classifier outputs the probability of a seizure for a sliding window (typically 2-10 seconds). At the postprocessing stage, smoothing and thresholding are applied to eliminate false positives. Some systems use transfer learning to adapt to a specific patient.
Helps doctors detect epilepsy faster by analyzing long EEG recordings; used in wearable devices for seizure prediction.
Some algorithms can predict a seizure minutes before it starts by analyzing gradual changes in EEG that are invisible to the naked eye.













