On the performance of AS-LMS based adaptive filter for reduction of motion artifacts from PPG signals

A Photoplethysmographic (PPG) signal is invariably corrupted with motion artifacts (MA) due to voluntary or involuntary movements of the patient. PPG is a non-invasive signal, used for the estimation of arterial blood oxygen saturation (SpO2), which helps the physician to know the hypoxic status of patient during clinical investigations. This paper presents an efficient Adaptive Step-size Least Mean Squares (AS-LMS) based adaptive filter for reducing the MA from corrupted PPG signals. The novelty of the proposed algorithm lies in the fact that a synthetic noise reference signal for adaptive filtering, representing MA noise, is generated internally from the MA corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Convergence analysis, SNR calculations and Statistical analysis revealed that the proposed AS-LMS algorithm has a clear edge over the Time-Varying Step-size LMS (TVS-LMS) and Constant Step-size LMS (CS-LMS) based adaptive algorithms for MA reduction from PPG signals. Experimental results, for the PPG data recorded with different motion artifacts (Horizontal, Vertical and Bending motion of finger), demonstrated the efficacy of the proposed algorithm in MA reduction and thus making it best suitable for real-time pulse oximetry applications.