Mathilde C Hermans1, M Brandon Westover2, Michel J A M van Putten3, Lawrence J Hirsch4, Nicolas Gaspard5. 1. Department of Technical Medicine, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands; Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA. 2. Department of Neurology, Massachusetts General Hospital, Boston, MA 02114-2622, USA. 3. Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente and Clinical Neurophysiology Group, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands. 4. Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA. 5. Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA; Department of Neurology, Comprehensive Epilepsy Center, Université Libre de Bruxelles - Hôpital Erasme, Route de Lennik, 808, 1070 Bruxelles, Belgium.
Abstract
OBJECTIVE: EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. METHODS: In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. RESULTS: The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case. CONCLUSION: Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. SIGNIFICANCE: Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity.
OBJECTIVE: EEG reactivity is an important predictor of outcome in comatosepatients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. METHODS: In EEG segments recorded during reactivity testing in 59 comatosepatients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. RESULTS: The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case. CONCLUSION: Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. SIGNIFICANCE: Quantitative EEG may be useful for evaluating reactivity in comatosepatients, offering increased objectivity.
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Authors: Wei-Long Zheng; Edilberto Amorim; Jin Jing; Ona Wu; Mohammad Ghassemi; Jong Woo Lee; Adithya Sivaraju; Trudy Pang; Susan T Herman; Nicolas Gaspard; Barry J Ruijter; Marleen C Tjepkema-Cloostermans; Jeannette Hofmeijer; Michel J A M van Putten; M Brandon Westover Journal: IEEE Trans Biomed Eng Date: 2022-04-21 Impact factor: 4.756