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Development of an Anesthesia Depth Monitoring Algorithm Based on Photoplethysmography and Electrocardiography

Deep learning methods for the prediction of depth of anesthesia with Photoplethysmography and Electrocardiography

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0010707
Enrollment
500
Registered
2025-07-03
Start date
2025-07-07
Completion date
Unknown
Last updated
2025-07-21

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

Kangbuk Samsung Medical Center
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients aged between 19 and 70 years who are undergoing elective surgery under general anesthesia and are classified as ASA (American Society of Anesthesiologists) physical status I to III

Exclusion criteria

Exclusion criteria: Patients who decline to participate Patients with a history of psychiatric disorders that may affect EEG measurements Patients with a known history of neurological disorders such as epilepsy or stroke Patients with dermatological conditions that prevent the attachment of ECG or BIS monitoring devices Pregnant women

Design outcomes

Primary

MeasureTime frame
Bispectral index—

Secondary

MeasureTime frame
Photoplethysmography;Electrocardiography—

Countries

Korea, Republic of

Contacts

Public ContactJaegeum Shim

Kangbuk Samsung Medical Center

jaegeum77@naver.com+82-2-2001-1667

Outcome results

None listed

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