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Mendeley readers
Attention Score in Context
| Title |
Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients
|
|---|---|
| Published in |
Frontiers in Neurology, June 2018
|
| DOI | 10.3389/fneur.2018.00454 |
| Pubmed ID | |
| Authors |
Johannes P. Koren, Johannes Herta, Franz Fürbass, Susanne Pirker, Veronika Reiner-Deitemyer, Franz Riederer, Julia Flechsenhar, Manfred Hartmann, Tilmann Kluge, Christoph Baumgartner |
| Abstract |
Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend). Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC1 and AC2 coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests. Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61-0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68-0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68-0.72), whereas the other two showed moderate agreement (0.45-0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57). Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 59 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 19% |
| Researcher | 11 | 19% |
| Other | 8 | 14% |
| Student > Master | 5 | 8% |
| Student > Bachelor | 3 | 5% |
| Other | 11 | 19% |
| Unknown | 10 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 16 | 27% |
| Computer Science | 6 | 10% |
| Engineering | 6 | 10% |
| Neuroscience | 6 | 10% |
| Nursing and Health Professions | 3 | 5% |
| Other | 6 | 10% |
| Unknown | 16 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2018.
All research outputs
#20,522,137
of 23,090,520 outputs
Outputs from Frontiers in Neurology
#9,014
of 12,007 outputs
Outputs of similar age
#287,383
of 328,040 outputs
Outputs of similar age from Frontiers in Neurology
#249
of 322 outputs
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