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Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

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Updated: Dec 20, 2025

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Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset.

Mark L Scheuer¹, Scott B Wilson¹, Arun Antony²

¹Persyst Development Corporation, Solana Beach, California, U.S.A.

Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society

|May 31, 2020

Summary

This summary is machine-generated.

This study found that the Persyst 14 seizure detection algorithm performed similarly to human experts in analyzing electroencephalogram (EEG) recordings from epilepsy monitoring units. The algorithm achieved comparable sensitivity and false-positive rates, marking a significant advancement in automated seizure detection.

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Area of Science:

Clinical Neuroscience
Medical Technology
Epilepsy Research

Background:

Automated seizure detection in electroencephalogram (EEG) recordings is crucial for epilepsy management.
Human expert performance in marking seizures can vary, necessitating objective evaluation methods.

Purpose of the Study:

To compare the seizure detection accuracy of human experts against computer algorithms using prolonged EEG recordings.
To evaluate the performance of the Persyst 14 algorithm relative to human expert agreement.

Main Methods:

120 prolonged EEG recordings, including 100 with reported seizures, were analyzed.
Seizures were marked by three human experts and two computer algorithms.
Pairwise sensitivity and false-positive rates were calculated and compared using a statistical modified Turing test.

Main Results:

Human experts showed variable pairwise sensitivity (mean 72.5%-84.9%) and low false-positive rates (0.4-1.0/day).
The Persyst 14 algorithm achieved 78.2% sensitivity and 1.0/day false-positive rate, comparable to human performance.
Statistical analysis indicated that Persyst 14 met noninferiority criteria compared to human experts.

Conclusions:

Human experts exhibit moderate agreement in seizure marking on prolonged EEG.
The Persyst 14 algorithm demonstrates statistically noninferior performance to human experts in seizure detection.
This study represents the first instance of a seizure detection algorithm performing comparably to human experts.