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The ENCEVIS algorithm in the EMU and the factors affecting its performance: Our experience.

Aleksandre Tsereteli1, Natela Okujava1,2, Nikoloz Malashkhia1

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Summary

ENCEVIS 1.7 shows reasonable seizure detection performance in epilepsy monitoring, with higher accuracy for focal seizures and longer durations. This automated tool can aid neurophysiologists by identifying seizure recordings, potentially reducing workload.

Keywords:
Automatic seizure detectionEpilepsyLong term video EEG monitoringSeizure

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Area of Science:

  • Clinical Neurophysiology
  • Medical Device Technology
  • Epilepsy Research

Background:

  • Long-term video electroencephalography (EEG) monitoring is crucial for epilepsy diagnosis and management.
  • Automated seizure detection systems aim to improve efficiency and accuracy in analyzing EEG data.
  • Evaluating the performance of new seizure detection algorithms is essential for clinical adoption.

Purpose of the Study:

  • To assess the seizure detection performance of the ENCEVIS 1.7 algorithm.
  • To identify factors influencing ENCEVIS 1.7's performance in detecting epileptic seizures.
  • To explore the potential utility of ENCEVIS 1.7 in long-term video EEG monitoring units.

Main Methods:

  • Analysis of 43 video-EEG recordings containing 112 epileptic seizures.
  • Defined and calculated true positive, false negative, and false positive seizure detections.
  • Investigated the influence of ictal pattern rhythmicity, seizure duration, patient age, and extracerebral signals on algorithm sensitivity.

Main Results:

  • ENCEVIS 1.7 demonstrated an overall sensitivity of 71.2%, with higher sensitivity for focal (75.1%) versus generalized seizures (62%).
  • Algorithm performance was influenced by rhythmic ictal patterns, longer seizure duration (>60 sec), and adult patient age (>18 years).
  • ENCEVIS achieved 79.1% accuracy in annotating recordings with at least one seizure, showing reasonable performance for specific seizure types.

Conclusions:

  • ENCEVIS 1.7 offers reasonable seizure detection capabilities, particularly for focal to bilateral tonic-clonic and temporal lobe onset seizures.
  • Factors such as rhythmic ictal patterns, extended seizure duration, and adult age enhance algorithm performance.
  • ENCEVIS can serve as a valuable tool for flagging seizure-containing recordings, potentially decreasing neurophysiologist workload.