Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy. (1st August 2015)

• Record Type: Journal Article
• Authors: Temko, Andriy; Doyle, Orla; Murray, Deirdre; Lightbody, Gordon; Boylan, Geraldine; Marnane, William
• Is Part Of: Computers in biology and medicine. Volume 63(2015)
• Page Start: 169
• Page End: 177
• Publication Date: 2015-08-01
• Subjects: Neonatal -- Multimodal -- EEG -- ECG -- Neurodevelopmental -- Outcome -- Decision support system; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.05.017
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Automated multimodal prediction of outcome in newborns with hypoxic-ischaemic encephalopathy is investigated in this work. Routine clinical measures and 1 h EEG and ECG recordings 24 h after birth were obtained from 38 newborns with different grades of HIE. Each newborn was reassessed at 24 months to establish their neurodevelopmental outcome. A set of multimodal features is extracted from the clinical, heart rate and EEG measures and is fed into a support vector machine classifier. The performance is reported with the statistically most unbiased leave-one-patient-out performance assessment routine. A subset of informative features, whose rankings are consistent across all patients, is identified. The best performance is obtained using a subset of 9 EEG, 2 h and 1 clinical feature, leading to an area under the ROC curve of 87% and accuracy of 84% which compares favourably to the EEG-based clinical outcome prediction, previously reported on the same data. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy. Highlights: We investigate the means to assist a clinician in prediction of neurodevelopmental outcome in newborns. We combine information from three different modalities – clinical, EEG and ECG. We identify a set of consistently informative features. Automated analysis of multimodal data complements clinical prediction.