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ASIC-based Fetal Heart Rate Sensing System using Dry/Capacitive Electrodes.

Yijing Zhang, Alessandra Galli, Myrthe van der Ven

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    This study introduces an integrated circuit (IC) for fetal heart rate (fHR) monitoring, enabling low-power, compact home use. The system effectively reduces motion artifacts, providing quality data for assessing fetal well-being.

    Area of Science:

    • Biomedical Engineering
    • Maternal-Fetal Medicine
    • Integrated Circuit Design

    Background:

    • Fetal heart rate (fHR) monitoring is crucial for fetal well-being assessment.
    • Home-based monitoring solutions are desirable for long-term, comfortable use.
    • Dry and capacitive electrodes show promise, but discrete component systems have limitations.

    Purpose of the Study:

    • To develop an application-specific integrated circuit (ASIC) for fHR monitoring.
    • To overcome limitations of discrete component systems with an integrated solution.
    • To enable comfortable, long-term, home-based fHR monitoring with reduced motion artifacts.

    Main Methods:

    • Designed a 16-channel recording ASIC with integrated motion artifact reduction (MAR) in 180nm CMOS.

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  • Developed a prototype system using the ASIC, a wearable garment, and an fHR extraction algorithm.
  • Validated the ASIC and prototype through electrical characterization and in-vitro/in-vivo experiments.

    • Main Results:

    • The ASIC achieved required performance with extended dynamic range and saturation prevention, occupying 12.5 mm² and consuming 450 μW.
    • Validation with four pregnant volunteers demonstrated sufficient data quality for fetal QRS-peak and fHR detection within recordings.
    • The system successfully acquired fHR data in real-world home monitoring scenarios.

    Conclusions:

    • The IC-based solution provides low power, a small form factor, and effective MAR for fHR monitoring.
    • This is the first IC-based system for dry/capacitive fHR monitoring, suitable for home applications.
    • The developed system is a promising candidate for improving remote fetal well-being assessment.