Rehan Ahmed1, Andriy Temko2, William Marnane2, Gordon Lightbody2, Geraldine Boylan3. 1. Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College Cork, Ireland. Electronic address: rehan@eleceng.ucc.ie. 2. Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College Cork, Ireland. 3. Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Pediatrics and Child Health, University College Cork, Ireland.
Abstract
OBJECTIVE: This work presents a novel automated system to classify the severity of hypoxic-ischemic encephalopathy (HIE) in neonates using EEG. METHODS: A cross disciplinary method is applied that uses the sequences of short-term features of EEG to grade an hour long recording. Novel post-processing techniques are proposed based on majority voting and probabilistic methods. The proposed system is validated with one-hour-long EEG recordings from 54 full term neonates. RESULTS: An overall accuracy of 87% is achieved. The developed grading system has improved both the accuracy and the confidence/quality of the produced decision. With a new label 'unknown' assigned to the recordings with lower confidence levels an accuracy of 96% is attained. CONCLUSION: The statistical long-term model based features extracted from the sequences of short-term features has improved the overall accuracy of grading the HIE injury in neonatal EEG. SIGNIFICANCE: The proposed automated HIE grading system can provide significant assistance to healthcare professionals in assessing the severity of HIE. This represents a practical and user friendly implementation which acts as a decision support system in the clinical environment. Its integration with other EEG analysis algorithms may improve neonatal neurocritical care.
OBJECTIVE: This work presents a novel automated system to classify the severity of hypoxic-ischemicencephalopathy (HIE) in neonates using EEG. METHODS: A cross disciplinary method is applied that uses the sequences of short-term features of EEG to grade an hour long recording. Novel post-processing techniques are proposed based on majority voting and probabilistic methods. The proposed system is validated with one-hour-long EEG recordings from 54 full term neonates. RESULTS: An overall accuracy of 87% is achieved. The developed grading system has improved both the accuracy and the confidence/quality of the produced decision. With a new label 'unknown' assigned to the recordings with lower confidence levels an accuracy of 96% is attained. CONCLUSION: The statistical long-term model based features extracted from the sequences of short-term features has improved the overall accuracy of grading the HIE injury in neonatal EEG. SIGNIFICANCE: The proposed automated HIE grading system can provide significant assistance to healthcare professionals in assessing the severity of HIE. This represents a practical and user friendly implementation which acts as a decision support system in the clinical environment. Its integration with other EEG analysis algorithms may improve neonatal neurocritical care.
Authors: Rhodri O Lloyd; John M O'Toole; Vicki Livingstone; William D Hutch; Elena Pavlidis; Anne-Marie Cronin; Eugene M Dempsey; Peter M Filan; Geraldine B Boylan Journal: Pediatr Res Date: 2016-04-18 Impact factor: 3.756
Authors: L Chalak; L Hellstrom-Westas; S Bonifacio; T Tsuchida; V Chock; M El-Dib; An N Massaro; A Garcia-Alix Journal: Semin Fetal Neonatal Med Date: 2021-07-28 Impact factor: 3.726
Authors: John M O'Toole; Geraldine B Boylan; Rhodri O Lloyd; Robert M Goulding; Sampsa Vanhatalo; Nathan J Stevenson Journal: Med Eng Phys Date: 2017-04-18 Impact factor: 2.242