Phase plane based identification of fetal heart rate patterns

Area of Science:

• Biomedical Engineering
• Fetal Physiology
• Signal Processing

Background:

• Fetal heart rate patterns (fHRP) are crucial for assessing fetal well-being.
• Current methods for fHRP classification have limitations in differentiating subtle patterns.
• Nijhuis criteria provide a standardized framework for classifying fHRP.

Purpose of the Study:

• To introduce and evaluate a novel phase plane analysis (PPA) for characterizing fHRP.
• To compare the performance of PPA against traditional metrics (SDNN, RMSSD) in classifying fHRP.
• To assess the ability of PPA to differentiate fHRP according to Nijhuis criteria.

Main Methods:

• Collected fetal magnetocardiogram (fMCG) data from 22 low-risk fetuses (30-37 weeks gestation).
• Utilized an adaptive Hilbert transform to identify R waves and calculate fetal heart rate.
• Applied PPA, SDNN, and RMSSD to fHRP data, with expert scoring based on Nijhuis criteria.

Main Results:

• Phase plane analysis (PPA) demonstrated high performance in differentiating all pairs of fHRP.
• PPA successfully distinguished between patterns A, B, and D as defined by Nijhuis criteria.
• Traditional methods (SDNN, RMSSD) showed lower performance in differentiating these patterns.

Conclusions:

• Phase plane analysis (PPA) is a superior method for characterizing fetal heart rate patterns (fHRP).
• PPA offers improved accuracy in classifying fHRP according to Nijhuis criteria compared to existing techniques.
• This technique holds promise for enhanced fetal well-being monitoring.