NeoGuard: A neonatal EEG monitor with real-time, bedside data visualization and automated decision support.

NeoGuard: A neonatal EEG monitor with real-time, bedside data visualization and automated decision support. - NeoGuard studie

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON38914

Enrollment

Registered

2013-09-11

Start date

2014-03-03

Completion date

Unknown

Last updated

2024-04-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

asphyxia = Oxygen depletion during birth and seizures= fits

Interventions

None listed

Eligibility

Age

2 Years to 11 Years

Inclusion criteria

Inclusion criteria: Term babies with an umbilical pH less than 7,1 APGAR score less than 6 at 5 minutes Thompson score more than 4. Term babies with clinical diagnosis of neonatal seizures

Exclusion criteria

Exclusion criteria: postmenstrual age less than 36 weeks, or postmenstrual age more then 36 weeks and at least one of the other criteria not fulfilled

Design outcomes

Primary

Measure	Time frame
To develop a first neonatal, fully automated and continuous brain monitoring to be used in the daily practice. The automated seizure detector diagnoses seizures with a sensitivity of more than 85% and less than 1 false alarm per 3 hours. With automated background EEG grading, the prediction of short term outcome (on basis of MRI scores)and long term outcome (on basis of follow-up at two years) can be made with a sensitivity of more than 85% and specificity of more than 80%. The following questions will be answered: is the software for automatic detection of seizures valuable? Therefore the alarms will be compared with the expert labelling of seizures seen on the classical EEG Objective quantification of the background EEG is important. The goal is to define important patterns in the background EEG (e.g. sleep-wake cycli, bursts, burst suppression, entropy measures, *).	—

Measure

Time frame

To develop a first neonatal, fully automated and continuous brain monitoring to be used in the daily practice. The automated seizure detector diagnoses seizures with a sensitivity of more than 85% and less than 1 false alarm per 3 hours. With automated background EEG grading, the prediction of short term outcome (on basis of MRI scores)and long term outcome (on basis of follow-up at two years) can be made with a sensitivity of more than 85% and specificity of more than 80%. The following questions will be answered: is the software for automatic detection of seizures valuable? Therefore the alarms will be compared with the expert labelling of seizures seen on the classical EEG Objective quantification of the background EEG is important. The goal is to define important patterns in the background EEG (e.g. sleep-wake cycli, bursts, burst suppression, entropy measures, *).

—

Secondary

Measure	Time frame
-not applicable	—

Countries

Netherlands

Outcome results