Key result
The combination of independent component analysis and block interleaving with low-pass filtering reduced motion artifacts in photoplethysmography signals during movement.
A novel algorithm combining independent component analysis and block interleaving with low-pass filtering effectively reduces motion artifacts in PPG signals, potentially improving arterial oxygen saturation measurement during movement.
May aid wearable SpO2 monitoring during activity; extends ICA-based artifact reduction but requires clinical validation.
Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.
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Kim et al. (2006) studied Motion artifacts in photoplethysmography (PPG) signals. Independent component analysis and block interleaving with low-pass filtering was evaluated on Reduction of motion artifacts. The combination of independent component analysis and block interleaving with low-pass filtering reduced motion artifacts in photoplethysmography signals during movement.
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