Quantitative electroencephalogram in term neonates under different sleep states
© 2023. The Author(s), under exclusive licence to Springer Nature B.V..
Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
|---|---|
| Enthalten in: |
Journal of clinical monitoring and computing - (2023) vom: 18. Okt. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yuan, Ian [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
EEG |
|---|
| Anmerkungen: |
Date Revised 18.10.2023 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1007/s10877-023-01082-6 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM36343674X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM36343674X | ||
| 003 | DE-627 | ||
| 005 | 20231226093339.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10877-023-01082-6 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1211.xml |
| 035 | |a (DE-627)NLM36343674X | ||
| 035 | |a (NLM)37851153 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yuan, Ian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantitative electroencephalogram in term neonates under different sleep states |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 18.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to Springer Nature B.V. | ||
| 520 | |a Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a EEG | |
| 650 | 4 | |a Neonatal EEG | |
| 650 | 4 | |a Neonatal sleep | |
| 650 | 4 | |a Quantitative EEG | |
| 700 | 1 | |a Georgostathi, Georgia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bingqing |e verfasserin |4 aut | |
| 700 | 1 | |a Hodges, Ashley |e verfasserin |4 aut | |
| 700 | 1 | |a Kurth, C Dean |e verfasserin |4 aut | |
| 700 | 1 | |a Kirschen, Matthew P |e verfasserin |4 aut | |
| 700 | 1 | |a Huh, Jimmy W |e verfasserin |4 aut | |
| 700 | 1 | |a Topjian, Alexis A |e verfasserin |4 aut | |
| 700 | 1 | |a Lang, Shih-Shan |e verfasserin |4 aut | |
| 700 | 1 | |a Richter, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Abend, Nicholas S |e verfasserin |4 aut | |
| 700 | 1 | |a Massey, Shavonne L |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of clinical monitoring and computing |d 1998 |g (2023) vom: 18. Okt. |w (DE-627)NLM095818405 |x 1573-2614 |7 nnns |
| 773 | 1 | 8 | |g year:2023 |g day:18 |g month:10 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s10877-023-01082-6 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 18 |c 10 | ||