OBJECTIVE: To test the hypothesis that quantitative EEG (qEEG) measures are associated with a grading of HIE based on the visual interpretation of neonatal EEG (EEG/HIE). METHODS: Continuous multichannel video-EEG data were recorded for up to 72 h. One-hour EEG segments from each recording were visually analysed and graded by two electroencephalographers (EEGers) blinded to clinical data. Several qEEG measures were calculated for each EEG segment. Kruskal-Wallis testing with post hoc analysis and multiple linear regression were used to assess the hypothesis. RESULTS: Fifty-four full-term infants with HIE were studied. The relative delta power, skewness, kurtosis, amplitude, and discontinuity were significantly different across four EEG/HIE grades (p<0.05). A linear combination of these qEEG measures could predict the EEG/HIE grade assigned by the EEGers with an accuracy of 89%. CONCLUSION: Quantitative analysis of background EEG activity has shown that measures based on the amplitude, frequency content and continuity of the EEG are associated with a visual interpretation of the EEG performed by experienced EEGers. SIGNIFICANCE: Identifying qEEG measures that can separate between EEG/HIE grades is an important first step towards creating a classifier for automated detection of EEG/HIE grades.
OBJECTIVE: To test the hypothesis that quantitative EEG (qEEG) measures are associated with a grading of HIE based on the visual interpretation of neonatal EEG (EEG/HIE). METHODS: Continuous multichannel video-EEG data were recorded for up to 72 h. One-hour EEG segments from each recording were visually analysed and graded by two electroencephalographers (EEGers) blinded to clinical data. Several qEEG measures were calculated for each EEG segment. Kruskal-Wallis testing with post hoc analysis and multiple linear regression were used to assess the hypothesis. RESULTS: Fifty-four full-term infants with HIE were studied. The relative delta power, skewness, kurtosis, amplitude, and discontinuity were significantly different across four EEG/HIE grades (p<0.05). A linear combination of these qEEG measures could predict the EEG/HIE grade assigned by the EEGers with an accuracy of 89%. CONCLUSION: Quantitative analysis of background EEG activity has shown that measures based on the amplitude, frequency content and continuity of the EEG are associated with a visual interpretation of the EEG performed by experienced EEGers. SIGNIFICANCE: Identifying qEEG measures that can separate between EEG/HIE grades is an important first step towards creating a classifier for automated detection of EEG/HIE grades.
Authors: Timothy J Mitchell; Jeffrey J Neil; John M Zempel; Liu Lin Thio; Terrie E Inder; G Larry Bretthorst Journal: Clin Neurophysiol Date: 2012-09-24 Impact factor: 3.708
Authors: Vladimir Matic; Perumpillichira Joseph Cherian; Ninah Koolen; Amir H Ansari; Gunnar Naulaers; Paul Govaert; Sabine Van Huffel; Maarten De Vos; Sampsa Vanhatalo Journal: Front Hum Neurosci Date: 2015-04-23 Impact factor: 3.169