An Automatic Method for Motion Artifacts Detection in Photoplethysmographic Signals Referenced With Electrocardiography Data



This work presents an automatic motion artifact
detection algorithm for photoplethysmographic (PPG) signals
with synchronized electrocardiography (ECG) data. 18 features
from time and frequency domain were extracted and used in a
support vector machine (SVM) for automatic classification. The
performance achieved by this method (SE: 87.5%, SP: 85.5%
and CR: 86.8%) proves that the information extracted from the
relationship between electrocardiography and PPG can be used
to identify segments of motion artifacts in PPG signals


Motion artifacts, Support Vector Machines, automatic classification, PPG, ECG.

Related Project

iCIS - Intelligent Computing in the Internet of Services


7th International Conference on BioMedical Engineering and Informatics, September 2014

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