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Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology

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Abstract

Background/Objective

Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset™, Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients.

Methods

We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10–20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision.

Results

Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865–0.999) and 0.909 (0.587–0.998), respectively, and for detecting PDs were 0.824 (0.566–0.926) and 0.970 (0.842–0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440–0.897) and 0.970 (0.842–0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5–142).

Conclusions

HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.

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Author information

Satoshi Egawa and Toru Hifumi were responsible for conception of the article and drafted and revised the manuscript. Yuichi Kubota and Yasuhiro Kuroda helped to make a study design and draft the manuscript. Hidetoshi Nakamoto helped to draft the manuscript. All authors have read and approved the final manuscript and take full responsibility for all aspects of the study.

Correspondence to Toru Hifumi.

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The authors declare a conflict of interest with Nihon Kohden as a cooperative research.

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The protocol and consent procedures were approved by the Institutional Review Board.

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Egawa, S., Hifumi, T., Nakamoto, H. et al. Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology. Neurocrit Care 32, 217–225 (2020). https://doi.org/10.1007/s12028-019-00863-9

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Keywords

  • Electroencephalography
  • Continuous electroencephalogram
  • Headset-type continuous video electroencephalography
  • Periodic discharges
  • Nonconvulsive status epilepticus