Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Maternal-Fetal Medicine

Background:

• Dry electrodes offer potential for long-term fetal heart rate (fHR) monitoring but yield lower signal-to-noise ratios and unique artifacts compared to wet electrodes.
• Existing algorithms for fHR estimation require adaptation to effectively process signals from dry, textile electrodes.

Purpose of the Study:

• To develop and validate a novel signal processing method for accurate fHR estimation using dry electrodes.
• To specifically address and remove artifacts characteristic of dry and capacitive electrode recordings, such as triboelectricity and amplitude modulations.

Main Methods:

• A hybrid hardware and software pre-processing step was designed to mitigate dry electrode artifacts.
• Maternal ECG removal via blind source separation, followed by fetal ECG enhancement and QRS complex identification.
• The algorithm was tested on semi-simulated and real data using experimental textile electrodes.

Main Results:

• The proposed method demonstrated superior performance over the benchmark algorithm in denoising signals and estimating fHR.
• Quantified improvements were observed in sensitivity, F1-score, and root-mean-square error metrics.
• The system proved robust against high-amplitude motion artifacts common in dry electrode acquisitions.

Conclusions:

• A dedicated signal processing system tailored to the characteristics of dry electrodes is essential for reliable and accurate fHR estimation.
• The developed method offers a significant advancement for non-invasive, long-term fetal monitoring using accessible textile-based dry electrodes.