WU-NEAT: A clinically validated, open-source MATLAB toolbox for limited-channel neonatal EEG analysis
| Autor: | Zachary A. Vesoulis, Siddharth V. Jain, Paul Gamble, Nathalie M. El Ters, Steve M. Liao, Amit M. Mathur |
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| Rok vydání: | 2020 |
| Předmět: |
Washington
Channel (digital image) Correlation coefficient Universities Computer science Health Informatics Electroencephalography Article 030218 nuclear medicine & medical imaging Eeg recording 03 medical and health sciences symbols.namesake 0302 clinical medicine medicine Humans Reliability (statistics) medicine.diagnostic_test business.industry Infant Newborn Infant Reproducibility of Results Pattern recognition Filter (signal processing) Pearson product-moment correlation coefficient Computer Science Applications symbols Artificial intelligence Spectral edge frequency business 030217 neurology & neurosurgery Software Infant Premature |
| Zdroj: | Comput Methods Programs Biomed |
| ISSN: | 1872-7565 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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