On the performance of Time Varying Step-size Least Mean Squares(TVS-LMS) adaptive filter for MA reduction from PPG signals

Clinical Investigation of hypoxic status of the patients requires accurate information about the heart rate and oxygen saturation of arterial blood. Pulse oximeters are widely used for monitoring these parameters by recording the raw pulse oximeter signal, namely Photoplethysmogram (PPG). The recorded PPG Signal acquired using PPG sensors are usually corrupted with Motion Artifacts (MA) due to the voluntary or involuntary movements of patient. Reduction of MA has received much attention in the literature over recent years. In this paper, we present an efficient adaptive filtering technique based on Time Varying Step-size Least Mean Squares (TVS-LMS) algorithm for MA reduction. The novelty of the method 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 noise reference signal generation. Convergence analysis, SNR calculations and Statistical analysis revealed that the proposed TVS-LMS method has a clear edge over the Constant Step-size LMS (CS-LMS) based adaptive filtering technique. Test results, on the PPG data recorded with different MAs, demonstrated the efficacy of the proposed TVS-LMS algorithm in MA reduction and thus making it best suitable for real-time pulse oximetry applications.