Background: Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB.
Methods: The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user-defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern classification. Inter/intra-rater reliability was assessed. For the SEF, calculations were made using the same fourteen recordings, first with the reference and then with the WU-NEAT algorithm. Results were compared using Pearson's correlation coefficient.
Results: For the aEEG algorithm, intra- and inter-rater reliability was 100% and 98%, respectively. For the SEF, the mean±SD Pearson correlation coefficient between algorithms was 0.96±0.04.
Conclusion: We have demonstrated a clinically-validated toolbox for generating the aEEG as well as calculating the SEF from EEG data. Open-source access will enable widespread use of common analytic algorithms which are device-independent and unlikely to become outdated as technology changes, thereby facilitating future collaborative research in neonatal EEG.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606381 | PMC |
| http://dx.doi.org/10.1016/j.cmpb.2020.105716 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Diagnostic Accuracy of the Persyst Automated Seizure Detector in the Neonatal Population.
J Integr Neurosci
August 2024
Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children, WC1N 3JH London, UK.
Article Synopsis
- Neonatal seizures are difficult to diagnose mainly because they are often detected only through electrographic means, with multichannel video continuous electroencephalography (cEEG) being the best method; however, access to this method can be limited outside regular hours.
- This study evaluated the effectiveness of the Persyst neonatal seizure detection algorithm (SDA) by analyzing cEEG recordings from neonates in intensive care at Great Ormond Street Hospital, comparing the SDA results to expert neurophysiology reports.
- Results showed that the Persyst neonatal SDA has high diagnostic accuracy with an area under the ROC curve of 0.94, indicating that it can reliably identify neonatal seizures using specific probability thresholds, achieving 80% sensitivity and 98
Electrographic monitoring for seizure detection in the neonatal unit: current status and future direction.
Pediatr Res
September 2024
Monash Newborn, Monash Children's Hospital, Melbourne, Australia.
Neonatal neurocritical intensive care is dedicated to safeguarding the newborn brain by prioritising clinical practices that promote early identification, diagnosis and treatment of brain injuries. The most common newborn neurological emergency is neonatal seizures, which may also be the initial clinical indication of neurological disease. A high seizure burden in the newborn period independently contributes to increased mortality and morbidity.
View Article and Find Full Text PDFClinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia.
Clin Neurophysiol
June 2024
BABA Center, Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland; Department of Clinical Neurophysiology, Epilepsia Helsinki, Full Member of ERN Epicare, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Objective: To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age.
Methods: The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death.
Neuromonitoring in neonatal critical care part I: neonatal encephalopathy and neonates with possible seizures.
Pediatr Res
July 2023
Division of Child Neurology, Stanford University, Palo Alto, CA, USA.
The blooming of neonatal neurocritical care over the last decade reflects substantial advances in neuromonitoring and neuroprotection. The most commonly used brain monitoring tools in the neonatal intensive care unit (NICU) are amplitude integrated EEG (aEEG), full multichannel continuous EEG (cEEG), and near-infrared spectroscopy (NIRS). While some published guidelines address individual tools, there is no consensus on consistent, efficient, and beneficial use of these modalities in common NICU scenarios.
View Article and Find Full Text PDFAn automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.
Lancet Digit Health
December 2022
BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
Background: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!