Novel tailoring algorithm for abrupt motion artifact removal in photoplethysmogram signals

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Photoplethysmogram (PPG) signals are crucial for wearable devices but are highly susceptible to motion artifacts (MAs).
• Existing MA detection and removal techniques often yield unsatisfactory results due to complex MA waveforms.
• Accurate PPG signal processing is essential for reliable health monitoring.

Purpose of the Study:

• To develop a novel framework for identifying and mitigating abrupt motion artifacts in PPG signals.
• To enhance the accuracy of physiological measurements derived from PPG.
• To provide a robust solution for MA removal that preserves signal integrity.

Main Methods:

• A new framework integrating feature extraction, change-point detection, and MA removal was proposed.
• A data-dependent mechanism for optimal frame-size determination was employed.
• The method was evaluated using heart-beat-rate-measurement applications.

Main Results:

• Achieved a 98% correct detection rate for motion artifacts (MAs).
• Demonstrated an average heart-beat-rate tracking accuracy exceeding 97%.
• Preserved the original signal's temporal structure, unlike spectrum-based methods.

Conclusions:

• The proposed framework effectively identifies and removes MAs from PPG signals.
• The method significantly improves heart rate measurement accuracy in wearable devices.
• The framework is adaptable for other PPG-based applications, such as blood oxygen level (SpO2) calculation.