Artifact reduction based on Empirical Mode Decomposition (EMD) in photoplethysmography for pulse rate detection

The pulstile components of photoplethysmography (PPG) contain valuable information about a subject's cardiovascular and metabolic systems. Pulse rate is one of the most significant vital signs that can be extracted from PPG signals. However, patient movement, especially movement at the measurement sites, such as fingers, can disturb the PPG's light path significantly, resulting in corrupted measurements. In this paper, a method is proposed for removing motion artifacts from PPG recordings. In this method, the Empirical Mode Decomposition (EMD) and Hilbert transform are used together to decompose PPG recordings into instantaneous frequency series on different scales of resolution. Motion artifacts and physiological signals are separated based on these series. The proposed method was used to recover PPG signals recorded in an experiment, where motion artifacts were intentionally introduced by finger bending. By using our method, the signal-to-noise ratio was increased from 0.078 dB of the contaminated signals to 0.318 dB, and the true detection rate of heartbeats was improved from 59.2% to 96.6%. The results demonstrated that the EMD combined with Hilbert transform has great potential in reducing motion artifacts in PPG signals and can improve the accuracy of heartbeat detection.