• Nandakumar Selvaraj, Yitzhak Mendelson, Kirk H Shelley, David G Silverman, and Ki H Chon.
• Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA. nselvaraj@wpi.edu
• Conf Proc IEEE Eng Med Biol Soc. 2011 Jan 1;2011:4972-5.

AbstractMotion and noise artifacts (MNA) have been a serious obstacle in realizing the potential of Photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a statistical approach based on the computation of kurtosis and Shannon Entropy (SE) for the accurate detection of MNA in PPG data. The MNA detection algorithm was verified on multi-site PPG data collected from both laboratory and clinical settings. The accuracy of the fusion of kurtosis and SE metrics for the artifact detection was 99.0%, 94.8% and 93.3% in simultaneously recorded ear, finger and forehead PPGs obtained in a clinical setting, respectively. For laboratory PPG data recorded from a finger with contrived artifacts, the accuracy was 88.8%. It was identified that the measurements from the forehead PPG sensor contained the most artifacts followed by finger and ear. The proposed MNA algorithm can be implemented in real-time as the computation time was 0.14 seconds using Matlab®.

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