Abstract
Performance Comparison of Variants of LMS Algorithms for Motion Artifact Removal in PPG Signals During Physical Activities
Highlights
Heart rate is one of the vital signs measured to monitor the health status of a person
We model the PPG signal with noise using an autoregressive integrated moving average (ARIMA) process of order (p,d,q)
In this paper we compared the performances of variants of Least Mean Square (LMS) algorithms for motion artifact removal in PPG signals during physical activities
Summary
Heart rate is one of the vital signs measured to monitor the health status of a person. The oldest method to measure heart rate is by taking the pulse It is still used all over the world. The light emitted from the photo diode travels through the tissue and is received by the receiver at the other side of the body. This method can be used at earlobes and undertips. LMS algorithm tries to minimize the Mean Square Error (MSE) cost function. The PPG signal corrupted with MA (dk) (k 2 Z) has the cardiac component (sk) which is the PPG signal without the MA and the noise component It is the most common among the LMS algorithms. The noise corrupted signal is expressed as, d=k sk + nk (1)
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