Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal

Measurement of photoplethysmograph signal plays an important role in medical applications. PPG is measured by an equipment named pulse oximeter which gives the oxygen saturation of arterial blood. The PPG signal measured by the pulse oximeter sensor is likely to be corrupted by motion artifacts created by hand movement or any other type of movement in the body. Movement artifacts become a major obstacle when measuring the oxygen saturation (SpO2) level. In recent years, many methods have been developed to reduce motion artifacts from PPG signal. This paper gives a comparison of five different signal processing methods implemented towards motion artifact reduction which include Adaptive Noise Cancellation (ANC), Wavelet Transform (WT), Independent Component Analysis (ICA), Singular Value Decomposition (SVD) and Cycle by Cycle Fourier Series Analysis (FSA). The results obtained after implementing the five methods revealed that SVD and FSA methods have given better results in terms of artifact reduction and also signal restoration. Performances of the five methods have been evaluated in terms of heart rate (HR) estimation error, mean ±standard deviation of peak to peak values of the enhanced PPG signal obtained after implementing all the five different methods.