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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

479
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:
479

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Automated spike and seizure detection: Are we ready for implementation?

E E M Reus1, G H Visser1, M P J Sommers-Spijkerman2

  • 1Stichting Epilepsie Instellingen Nederland (SEIN).

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Summary

Implementing automated spike and seizure detection in Epilepsy Monitoring Units (EMUs) requires addressing user trust and job security concerns. Overcoming these barriers can enhance EEG workflow efficiency and data quantification.

Keywords:
Automated detectionAutomatic detectionEEGEpilepsy monitoring unitSpike and seizure detection

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Automated detection of spikes and seizures in electroencephalography (EEG) has been researched for decades.
  • Despite advances, automated detection is not standard practice in Epilepsy Monitoring Units (EMUs).
  • Implementation of automated detection software in EMUs faces adoption challenges.

Purpose of the Study:

  • To identify barriers and enablers for implementing automated spike and seizure detection software in an EMU setting.
  • To understand user perspectives on adopting new automated EEG analysis tools.
  • To inform strategies for successful integration of automated detection technology.

Main Methods:

  • A qualitative study involving 22 semi-structured interviews.
  • Participants included 14 technicians and neurologists involved in EEG recording/reporting and 8 neurologists receiving EEG reports.
  • Study adherence to Consolidated Criteria for Reporting Qualitative Studies (COREQ).

Main Results:

  • 14 barriers and 14 enablers for implementation were identified.
  • Technicians reported most barriers, primarily lack of trust in software accuracy (seizure detection, false positives).
  • Concerns about skill degradation and job loss were noted; enablers included workflow efficiency, quantification, and willingness to adopt.

Conclusions:

  • User perspectives are crucial for successful implementation of automated spike and seizure detection in EMUs.
  • Addressing technician trust and job security concerns is vital.
  • Leveraging potential efficiency gains and quantification benefits can facilitate adoption.