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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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Related Experiment Video

Updated: Sep 11, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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Published on: July 14, 2023

Detecting poststroke epilepsy in nationwide administrative data: A validation study using Swedish registers.

André Idegård^1,2, David Larsson^1,2,3

¹Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.

|August 12, 2025

Summary

Validated algorithms using Swedish administrative data can reliably identify poststroke epilepsy (PSE). This supports large-scale research on epilepsy treatment and outcomes, though local validation is advised.

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Area of Science:

Neurology
Public Health
Health Informatics

Background:

Healthcare administrative data rely on the International Classification of Diseases (ICD) system.
ICD lacks specific codes for epilepsy etiological subgroups.
Validating methods for identifying poststroke epilepsy (PSE) in administrative data is crucial.

Purpose of the Study:

To validate algorithms for identifying poststroke epilepsy (PSE) in Swedish administrative data.
To assess the accuracy of administrative data for PSE case identification.

Main Methods:

Developed algorithms using combinations of ICD-10 codes for stroke and seizures, with some including antiseizure medication (ASM) prescriptions.
Focused on positive predictive values (PPVs) with medical records as the reference standard.
Analyzed data from the National Patient Register for individuals with stroke and subsequent seizure codes, using deceased patients for record review.

Main Results:

Positive predictive values (PPVs) for PSE identification ranged from 84.1% to 92.5%.
Relative coverage varied from 60% to 89% depending on the algorithm's inclusivity.
Data from 321 deceased patients (median age 78) were analyzed, showing no significant differences in characteristics.

Conclusions:

Administrative data algorithms can reliably identify PSE cases for large-scale studies.
Stricter algorithms improve accuracy but reduce case detection (coverage).
Local validation is necessary before applying these algorithms in other healthcare systems.