Detection of hemodynamic adaptations during impending syncope: implementation of a robust algorithm based on pulse...

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Medical Device Technology

Background:

• Syncope, or fainting, is a significant public health concern linked to injuries from falls.
• Previous research established the feasibility of syncope prediction using Pulse Arrival Time (PAT) analysis.
• Existing prediction algorithms are vulnerable to measurement noise, particularly Photoplethysmography (PPG) artifacts, leading to inaccurate detections.

Purpose of the Study:

• To develop a robust algorithm for syncope prediction that effectively handles measurement artifacts.
• To improve the accuracy and reliability of early syncope detection.
• To validate the algorithm's performance in a clinical setting using head-up tilt table testing.

Main Methods:

• Development of an algorithm concept focused on tracking relative changes in PAT to detect syncope onset.
• Implementation of artifact handling mechanisms to address noise, specifically PPG artifacts.
• Testing the algorithm's performance on patient data acquired during head-up tilt table testing.

Main Results:

• The proposed algorithm demonstrates improved detection performance for impending syncope.
• The method effectively mitigates the impact of PPG artifacts on syncope detection.
• Relative PAT change tracking proves a viable strategy for robust syncope prediction.

Conclusions:

• The novel algorithm enhances syncope prediction by focusing on relative PAT changes and artifact reduction.
• This approach offers a more reliable method for early detection of syncope, potentially reducing associated injuries.
• The findings suggest improved syncope prediction capabilities in clinical settings like head-up tilt table testing.