Advanced EMD method using variance characterization for PPG with motion artifact

Motion Artifact (MA) reduction is an important part in Photoplethysmography (PPG) signal processing for wearing devices. The heavily-corrupted period in PPG can hardly be rebuilt by frequency domain methods. This paper proposes an advanced time-frequency analysis method based on Empirical Mode Decomposition (EMD) using variance characterization of extrema. In this way, the computing costs are largely decreased by picking out the corrupted period, while the wave clusters found in it for estimation can help reduce error detection rate. The result shows our method is accurate in pulse rate estimation for heavily-corrupted PPG signals. The average relative error of our method is 1.03%, which is a result of data from PhysioBank MIMIC II waveform database.