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Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise

Sayeed Shafayet Chowdhury, Rakib Hyder, Md Samzid Bin Hafiz

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    |November 29, 2016
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    This study introduces MURAD, a new method for accurate heart rate (HR) monitoring from wrist-worn photoplethysmographic (PPG) signals. MURAD effectively reduces motion artifacts (MA) for reliable HR estimation in wearable devices.

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    Area of Science:

    • Biomedical Engineering
    • Wearable Technology
    • Signal Processing

    Background:

    • Photoplethysmographic (PPG) signals from wrist-based sensors are crucial for wearable devices.
    • Motion artifacts (MA) significantly degrade PPG signal quality, complicating accurate heart rate (HR) estimation.
    • Robust HR estimation algorithms are vital for the commercial success of wearable health monitors.

    Purpose of the Study:

    • To develop a robust algorithm for accurate HR estimation from wrist-type PPG signals.
    • To address the challenge of severe motion artifacts (MA) in PPG-based HR monitoring.
    • To enhance the user experience and reliability of wearable devices.

    Main Methods:

    • Proposed a novel Multiple Reference Adaptive Noise Cancellation (MURAD) technique.
    • Utilized four reference noise signals (RNS): three-axis accelerometer data and the difference between two PPG signals.
    • Employed peak verification techniques and a time-window approach for HR estimation.

    Main Results:

    • The MURAD technique demonstrated a lower average absolute error compared to existing state-of-the-art methods.
    • Achieved more accurate HR estimations even under severe motion artifact conditions.
    • Successfully reduced the impact of complex motion artifacts on PPG signals.

    Conclusions:

    • MURAD offers a promising solution for reliable HR monitoring using PPG in wearable devices.
    • The method effectively mitigates motion artifacts, improving the accuracy of HR estimation.
    • Enhances the feasibility and user experience of wrist-based PPG monitoring devices.