FindTrial
Sign In

Development of anesthesia depth estimation system using Electroencephalogram

Development of Highly Reliable Anesthesia Estimation System Using Artificial Neural Networks Based on Electroencephalogram

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0005848
Enrollment
250
Registered
2021-02-01
Start date
2021-03-01
Completion date
Unknown
Last updated
2024-07-23

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

Conditions

None listed

Interventions

None listed

Sponsors

Chungnam National University Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients undergoing sedation after regional anesthesia

Exclusion criteria

Exclusion criteria: Patient refusal Uncontrolled DM, HTNe Significant cardiopulmonary disease, Psychiatric disease. Stroke or cerebrovascular disease Patients with renal dysfunction of CKD stage 4 or higher BMI 30 kg/m2

Design outcomes

Primary

MeasureTime frame
electroencephalogram—

Secondary

MeasureTime frame
Hemodynamic parameters—

Countries

Korea, Republic of

Contacts

Public ContactJiho Park

Chungnam National University Hospital

jihopark@naver.com+82-42-280-7840

Outcome results

None listed

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