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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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    Summary
    This summary is machine-generated.

    This study introduces accelerated Hurst Contour Projection from Multiscale Multifractal Analysis (HCP-MMA) for real-time ECG arrhythmia classification. The optimized method achieves high accuracy and enables deployment on embedded systems for advanced diagnostics.

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    Area of Science:

    • Biomedical Engineering
    • Computational Science
    • Data Science

    Background:

    • Traditional heart rate variability analysis has limitations in capturing complex scaling patterns, leading to diagnostic inconsistencies.
    • Multifractal analysis offers a more comprehensive approach but suffers from high computational costs, hindering real-time applications.
    • Hurst Contour Projection from Multiscale Multifractal Analysis (HCP-MMA) enhances interpretability but requires significant computational resources.

    Purpose of the Study:

    • To develop a real-time implementation of HCP-MMA for improved ECG arrhythmia classification.
    • To overcome the computational limitations of traditional multifractal analysis for practical diagnostic use.
    • To enable the deployment of advanced multifractal analysis techniques in resource-constrained environments.

    Main Methods:

    • Implemented a runtime-optimized parallel computing pipeline for HCP-MMA, utilizing singular value decomposition (SVD) and vectorized processing.
    • Integrated the accelerated HCP-MMA with the AlexNet deep learning model for ECG arrhythmia classification.
    • Validated the approach on PhysioNet, MIT-BIH, and CU benchmark datasets, and tested real-time performance on a Raspberry Pi 5.

    Main Results:

    • Achieved a $730\times$ speedup over baseline MMA implementation on Intel systems.
    • Attained over 98% classification accuracy and up to 99.3% F1-score for ECG arrhythmia classification.
    • Demonstrated a $\sim 199\times$ speedup on embedded hardware (Raspberry Pi 5) with low inference time (0.0668s) and memory footprint (220 MB).

    Conclusions:

    • The accelerated HCP-MMA framework provides a computationally efficient and accurate solution for real-time ECG arrhythmia classification.
    • The method's robustness and generalizability were statistically validated (p < 0.05).
    • This advancement facilitates the integration of sophisticated multifractal analysis into wearable devices, telemedicine, and other real-time physiological monitoring applications.