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    This study introduces an application-specific integrated circuit (ASIC) for A-mode ultrasound arterial distension monitoring. It uses a 1-D convolutional neural network (CNN) for accurate artery identification and diameter measurement, achieving high performance in a compact design.

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    Area of Science:

    • Biomedical Engineering
    • Medical Devices
    • Ultrasound Technology

    Background:

    • Arterial distension monitoring is crucial for cardiovascular health assessment.
    • Existing ultrasound systems can be bulky and complex for continuous physiological monitoring.
    • Need for miniaturized, high-accuracy ultrasound solutions for portable instrumentation.

    Purpose of the Study:

    • To develop an application-specific integrated circuit (ASIC) for A-mode ultrasound arterial distension monitoring.
    • To integrate echo pattern recognition and arterial diameter waveform reconstruction onto a single chip.
    • To achieve accurate probe positioning and arterial wall localization using a convolutional neural network (CNN).

    Main Methods:

    • Designed and fabricated a mixed-signal ASIC for A-mode ultrasound.
    • Employed a 1-D CNN with gradient-weighted class activation mapping (Grad-CAM) for echo pattern recognition and arterial wall localization.
    • Integrated a high-voltage pulser, T/R switch, analog front-end, and digital post-processing circuits.
    • Fabricated the ASIC using a 180-nm BCD process.

    Main Results:

    • The ASIC achieved 95% CNN inference accuracy for echo pattern recognition.
    • Arterial distension estimation showed a high Pearson correlation coefficient (r) of 0.895.
    • The fabricated ASIC has a small active area (2.8 mm²) and low power consumption (1.65 mW).

    Conclusions:

    • The proposed ASIC enables accurate arterial distension monitoring using A-mode ultrasound.
    • The integrated CNN and Grad-CAM provide robust artery identification and measurement.
    • The compact and efficient mixed-signal architecture demonstrates high feasibility for small-footprint physiological instrumentation.