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Waveform Similarity Analysis Using Graph Mining for the Optimization of Sensor Positioning in Wearable

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    Optimizing wearable seismocardiogram (SCG) sensor placement is crucial. This study introduces a graph-theory method to find the best position on the mitral valve for consistent SCG waveform measurements.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Wearable Technology

    Background:

    • Wearable seismocardiogram (SCG) devices face challenges with waveform variability due to sensor positioning and lack of standardized measurement protocols.
    • Consistent SCG signal acquisition is vital for reliable analysis in research and clinical settings.

    Purpose of the Study:

    • To develop and validate a method for optimizing sensor positioning in wearable seismocardiography.
    • To establish a standardized procedure for SCG measurements by maximizing waveform similarity.

    Main Methods:

    • A graph-theoretical model was designed to evaluate SCG signal similarity.
    • The method was applied to optical wearable sensors placed at mitral and aortic valve auscultation sites on 11 healthy subjects.
    • Inter-position and inter-posture analyses were conducted to assess waveform repeatability.

    Main Results:

    • The highest similarity among seismocardiogram waveforms was achieved with the sensor positioned over the mitral valve area.
    • Optimal waveform similarity was observed when subjects were in a lying-down posture.
    • The proposed algorithm effectively estimated waveform similarity and outperformed existing methods.

    Conclusions:

    • The developed approach offers a significant advancement in optimizing sensor positioning for wearable seismocardiography.
    • The method provides an effective means to estimate waveform similarity, crucial for reliable SCG data.
    • These findings can inform the design of improved SCG recording protocols for future studies and clinical applications.