A Clinically Oriented Framework for Real-Time Heart Rate Variability Analysis: A Novel Approach To Personalized and...

Area of Science:

• Physiological monitoring
• Computational biology
• Clinical informatics

Background:

• Heart rate variability (HRV) reflects autonomic nervous system activity and predicts clinical outcomes.
• Real-time HRV monitoring is clinically valuable but challenged by inter-individual variability and artifacts.
• Existing HRV tools often lack personalization and artifact management for bedside use.

Purpose of the Study:

• To develop and validate a computational framework for robust, personalized, real-time HRV analysis for clinical applications.
• To integrate adaptive alerting, artifact exclusion, and multi-scale visualization for bedside monitoring.
• To address limitations of current HRV analysis tools in clinical settings.

Main Methods:

• Developed a computational framework for simultaneous time- and frequency-domain HRV analysis.
• Incorporated an adaptive alert algorithm using patient-specific data (interquartile range).
• Implemented workflow-integrated artifact annotation and exclusion, plus multi-scale visualization.

Main Results:

• Validated the framework using ECG databases and synthetic signals, confirming robust R-wave detection.
• Demonstrated operational validation at the bedside with newborn patient ECG data.
• The system successfully combined personalized alerting, artifact exclusion, and multi-scale visualization.

Conclusions:

• The developed framework offers a significant advancement for real-time HRV analysis in clinical practice.
• It systematically addresses personalization and artifact management challenges for bedside monitoring.
• This work facilitates the integration of HRV into routine vital sign management for improved patient outcomes.