J Herta1, J Koren2, F Fürbass3, M Hartmann3, A Gruber4, C Baumgartner5. 1. Department of Neurosurgery, Medical University of Vienna, Vienna, Austria. Electronic address: johannes.herta@meduniwien.ac.at. 2. Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, 2nd Neurological Department, General Hospital Hietzing with Neurological Center Rosenhuegel, Vienna, Austria. 3. AIT Austrian Institute of Technology GmbH, Digital Safety & Security Department, Vienna, Austria. 4. Department of Neurosurgery, Medical University of Vienna, Vienna, Austria. 5. Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, 2nd Neurological Department, General Hospital Hietzing with Neurological Center Rosenhuegel, Vienna, Austria; Department of Epileptology and Clinical Neurophysiology, Sigmund Freud University, Vienna, Austria.
Abstract
OBJECTIVE: To investigate the effect of systematic electrode reduction from a common 10-20 EEG system on pattern detection sensitivity (SEN). METHODS: Two reviewers rated 17130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. RESULTS: High automated baseline SENs (84.99-93.39%) and SPEs (90.05-95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the "hairline+vertex" montage. While the "forehead+behind ear" montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. CONCLUSION: For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. SIGNIFICANCE: Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.
OBJECTIVE: To investigate the effect of systematic electrode reduction from a common 10-20 EEG system on pattern detection sensitivity (SEN). METHODS: Two reviewers rated 17130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. RESULTS: High automated baseline SENs (84.99-93.39%) and SPEs (90.05-95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the "hairline+vertex" montage. While the "forehead+behind ear" montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. CONCLUSION: For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. SIGNIFICANCE: Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.
Authors: Michael Müller; Andrea O Rossetti; Rebekka Zimmermann; Vincent Alvarez; Stephan Rüegg; Matthias Haenggi; Werner J Z'Graggen; Kaspar Schindler; Frédéric Zubler Journal: Crit Care Date: 2020-12-07 Impact factor: 9.097
Authors: Farrokh Manzouri; Marc Zöllin; Simon Schillinger; Matthias Dümpelmann; Ralf Mikut; Peter Woias; Laura Maria Comella; Andreas Schulze-Bonhage Journal: Front Neurol Date: 2022-03-04 Impact factor: 4.003