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Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A

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Machine learning effectively detects motion artifacts (MAs) in photoplethysmogram (PPG) signals without reference data. This review synthesizes these methods, highlighting limitations and the need for standardized validation for real-world applications.

Keywords:
computational complexitymachine learningmotion artifactsphotoplethysmogramreal-time applicationsreference signal-less methods

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Area of Science:

  • Biomedical Signal Processing
  • Artificial Intelligence in Healthcare
  • Wearable Sensor Technology

Background:

  • Photoplethysmogram (PPG) signals are susceptible to motion artifacts (MAs), compromising data integrity.
  • Machine learning (ML) offers promising solutions for MA detection without requiring reference signals.
  • Existing literature lacks a comprehensive synthesis and critical evaluation of ML-based MA detection methods in PPG.

Purpose of the Study:

  • To provide a narrative review of ML techniques for reference signal-less MA detection in PPG.
  • To critically analyze the limitations and inconsistencies in current research methodologies.
  • To guide researchers and developers in selecting and validating appropriate MA detection approaches.

Main Methods:

  • Systematic literature search focusing on ML algorithms for MA detection in PPG.
  • Exclusion of studies using signal filtering/decomposition without prior MA identification.
  • Exclusion of studies employing multi-channel or additional sensor data (e.g., accelerometers).

Main Results:

  • ML demonstrates significant potential for MA detection in PPG signals.
  • Current research exhibits limitations in model development, testing, and real-time applicability assessment.
  • Inconsistencies exist in validation measures and experimental standardization across studies.

Conclusions:

  • There is a critical need for standardized experimental protocols for validating ML-based MA detection in PPG.
  • Broader validation across diverse body parts and real-world scenarios is essential.
  • Objective assessment of method reliability and applicability is required for practical implementation.