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Vikash Shaw, Quoc Cuong Ngo, Nemuel D Pah

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    Summary

    This study introduces a new pipeline to automatically detect and remove artifacts from photoplethysmogram (PPG) recordings. This improves signal quality for accurate computerised analysis, especially in sleep studies.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing

    Background:

    • Photoplethysmogram (PPG) signals offer valuable physiological insights but are often corrupted by artifacts, limiting their computerised analysis.
    • Existing methods struggle with artifact removal, hindering the full potential of PPG in applications like sleep studies.

    Purpose of the Study:

    • To develop and validate an automated preprocessing pipeline for detecting and removing artifact-corrupted segments in PPG recordings.
    • To enhance the quality of PPG data for reliable computerised analysis.

    Main Methods:

    • PPG signals were segmented into 30-second intervals and processed using bandpass filtering.
    • A template of noise-free signal was generated and correlated with all segments to identify high-quality data.
    • The algorithm's performance was evaluated against a manually labeled ground truth of 14,400 segments.

    Main Results:

    • The developed algorithm achieved high accuracy (98.03%), precision (98.58%), sensitivity (98.95%), F1 score (98.76%), and specificity (94.43%).
    • The pipeline effectively distinguished between noise-free and artifact-containing segments.

    Conclusions:

    • The proposed automated pipeline significantly improves PPG signal quality by removing artifacts.
    • This method facilitates robust computerised analysis of PPG data, with direct clinical relevance for sleep studies and assessment.