Dataset Information

EEG, behavioural and physiological recordings following a painful procedure in human neonates.

ABSTRACT: We present a dataset of cortical, behavioural, and physiological responses following a single, clinically required noxious stimulus in a neonatal sample. Cortical activity was recorded from 112 neonates (29-47 weeks gestational age at study) using a 20-channel electroencephalogram (EEG), which was time-locked to a heel lance. This data is linked to pain-related behaviour (facial expression), physiology (heart rate, oxygenation) and a composite clinical score (Premature Infant Pain Profile, PIPP). The dataset includes responses to non-noxious sham and auditory controls. The infants' relevant medical and pain history was collected up to the day of the study and recorded in an extensive database of variables including clinical condition at birth, diagnoses, medications, previous painful procedures, injuries, and selected maternal information. This dataset can be used to investigate the cortical, physiological, and behavioural pain-related processing in human infants and to evaluate the impact of medical conditions and experiences upon the infant response to noxious stimuli. Furthermore, it provides information on the formation of individual pain phenotypes.

SUBMITTER: Jones L

PROVIDER: S-EPMC6233256 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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EEG, behavioural and physiological recordings following a painful procedure in human neonates.

Jones Laura L Laudiano-Dray Maria Pureza MP Whitehead Kimberley K Verriotis Madeleine M Meek Judith J Fitzgerald Maria M Fabrizi Lorenzo L

Scientific data 20181113

We present a dataset of cortical, behavioural, and physiological responses following a single, clinically required noxious stimulus in a neonatal sample. Cortical activity was recorded from 112 neonates (29-47 weeks gestational age at study) using a 20-channel electroencephalogram (EEG), which was time-locked to a heel lance. This data is linked to pain-related behaviour (facial expression), physiology (heart rate, oxygenation) and a composite clinical score (Premature Infant Pain Profile, PIPP) ...[more]

PMID: 30422128

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Project description:BackgroundEEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.MethodsOur fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.ResultsThe best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).DiscussionApart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

Dataset Information

EEG, behavioural and physiological recordings following a painful procedure in human neonates.

Publications

EEG, behavioural and physiological recordings following a painful procedure in human neonates.

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