Automated Author ProfileLindeman, R.
Lindeman, R.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Background: Spectral EEG analysis using automated quantification of total absolute band power (tABP) for long-term brain monitoring is reliable. We hypothesised that tABP during the first critical days of life could be a useful tool for predicting later developmental outcomes. Objective: To determine whether measuring EEG background activity in premature infants with automated tABP quantification during the first 3 days of life correlated with their developmental outcomes at 24 months. Methods: Preterm infants (group 1, gestational age, GA 24–28 weeks and group 2, GA 28–31 weeks) were continuously monitored by EEG for 3 days after birth. Their developmental outcomes were assessed using the Bayley-II and Peabody-2 developmental tests at 24 months. Their respective indices were calculated. Normal (index ≥85) and abnormal (index <85) outcomes were correlated with the tABP. Results: In group 1, the tABP was significantly lower in the abnormal infants than in the normal infants. The specificity and negative predictive value were also high for all of the tests that were applied in this group. In group 2, there was no correlation between the tABP and developmental outcome. Conclusion: This study found that extremely premature infants with poor developmental outcomes had significantly lower tABP values in their first days of life compared to infants from the same group with normal outcomes. This method may be useful in predicting later outcomes in extremely premature infants and has the advantage of being automated.
Authors
- Schumacher, E.M. ;
- Larsson, P.G. ;
- Sinding-Larsen, C. ;
- Aronsen, R. ;
- Lindeman, R. ;
- Skjeldal, O.H. ;
- Stiris, T.A.
Background: Spectral EEG analysis using automated quantification of total absolute band power (tABP) for long-term brain monitoring is reliable. We hypothesised that tABP during the first critical days of life could be a useful tool for predicting later developmental outcomes. Objective: To determine whether measuring EEG background activity in premature infants with automated tABP quantification during the first 3 days of life correlated with their developmental outcomes at 24 months. Methods: Preterm infants (group 1, gestational age, GA 24–28 weeks and group 2, GA 28–31 weeks) were continuously monitored by EEG for 3 days after birth. Their developmental outcomes were assessed using the Bayley-II and Peabody-2 developmental tests at 24 months. Their respective indices were calculated. Normal (index ≥85) and abnormal (index <85) outcomes were correlated with the tABP. Results: In group 1, the tABP was significantly lower in the abnormal infants than in the normal infants. The specificity and negative predictive value were also high for all of the tests that were applied in this group. In group 2, there was no correlation between the tABP and developmental outcome. Conclusion: This study found that extremely premature infants with poor developmental outcomes had significantly lower tABP values in their first days of life compared to infants from the same group with normal outcomes. This method may be useful in predicting later outcomes in extremely premature infants and has the advantage of being automated.
Authors
- Schumacher, E.M. ;
- Larsson, P.G. ;
- Sinding-Larsen, C. ;
- Aronsen, R. ;
- Lindeman, R. ;
- Skjeldal, O.H. ;
- Stiris, T.A.