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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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A generalized framework for pacing artifact removal using a Hampel filter.

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    Summary
    This summary is machine-generated.

    This study introduces a new semi-automated method to remove pacing artifacts from bioelectrical signals. The auto-regression approach proved more accurate for reconstructing signals, improving analysis of cardiac and gastric pacing data.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Neuroscience

    Background:

    • Cardiac pacing is a common therapy for irregular heart rhythms.
    • Pacing techniques also show promise for treating gastric motility disorders.
    • Stimulus artifacts in pacing studies hinder accurate signal analysis.

    Purpose of the Study:

    • To develop a semi-automated method for detecting and reconstructing pacing artifacts.
    • To compare the performance of auto-regressive and weighted mean methods for artifact reconstruction.
    • To improve the analysis of bioelectrical signals in pacing studies.

    Main Methods:

    • A Hampel filter was used for semi-automated artifact detection.
    • Two artifact reconstruction methods were employed: auto-regression and weighted mean.
    • The framework was validated on synthetic and in vivo cardiac and gastric pacing data.

    Main Results:

    • The auto-regression method achieved a lower mean RMS difference compared to the weighted mean method.
    • The auto-regression approach provided more accurate artifact reconstruction.
    • The developed framework effectively isolated evoked bioelectrical events from artifacts.

    Conclusions:

    • The proposed semi-automated framework accurately removes pacing artifacts, enhancing signal analysis.
    • Auto-regression is a superior method for reconstructing signals after pacing artifacts.
    • This advancement facilitates more efficient analysis of preclinical pacing data, aiding clinical therapy development.