Identification of different sedation levels based on automatic EEG-background detection in premature infants: a machine learning study.

Identification of different sedation levels based on automatic EEG-background detection in premature infants: a machine learning study. - Sedation monitoring in premature infants

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00028554

Enrollment

Registered

2022-03-22

Start date

2022-05-01

Completion date

Unknown

Last updated

2025-04-07

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

Conditions

Pain Sedation

Interventions

Group 1: EEG measurement prea/post sedation combined with NPASS (Pain and Sedation Scale) scoring

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: • Preterm infants born at a gestational age below 37 weeks. • Signed informed consent from parents or legal guardians.

Exclusion criteria

Exclusion criteria: • Infants with known congenital anomalies. • Parents or legal guardians deny informed consent.

Design outcomes

Primary

Measure	Time frame
The overarching aim of this study is to use deep-machine-learning algorithms for the interpretation of sudden changes in the EEG-background activity related to the administration of sedation.	—

Secondary

Measure	Time frame
- Contextualizing automatic EEG-background changes related to sedation with the clinical opinion of sedation expressed by the scoring of the Neonatal, Pain, Agitation and Sedation Scale (N-PASS).	—

Countries

Austria

Contacts

Public ContactVito Giordano

Universitätsklinik für Kinderund Jugendheilkunde

vito.giordano@meduniwien.ac.at+43 1 404003232

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026