A patient agitation early warning monitoring system and device
By combining multimodal blood oxygenation monitoring technology with electrocardiogram signals, separating arterial and venous blood flow from static tissue components, extracting blood flow fluctuation characteristics, and generating agitation risk indicators, the technology solves the problems of lag and lack of continuity in patient agitation assessment in existing technologies, and achieves early and objective agitation risk warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- QILU HOSPITAL(QINGDAO) CHEELOO COLLEGE OF MEDICINE SHANDONG UNIV
- Filing Date
- 2026-06-15
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for assessing patient agitation rely on subjective scales, which leads to lag and lack of continuity. Monitoring techniques based on conventional physiological parameters cannot accurately analyze the dynamic characteristics of microcirculation perfusion that are closely related to agitation precursors, making it difficult to achieve early and objective risk warnings.
A multimodal calibration module is used to acquire multimodal blood supply and oxygenation time-series monitoring sequences. The monitoring and calibration are performed by combining standard blood oxygenation phantoms and electrocardiogram reference signals. The arterial and venous blood flow components are separated from the static tissue components by the unmixing correction module. The feature scoring module extracts blood flow fluctuation features. The fusion assessment module generates agitation risk indicators. The agitation degree index vector is generated by multimodal gating weighted fusion and outputs assessment and early warning.
It enables non-invasive, continuous, and highly specific patient agitation risk assessment, improves the sensitivity of identifying physiological changes that are precursors to agitation, provides early and objective risk warnings, and supports intelligent clinical monitoring.
Smart Images
Figure CN122417431A_ABST