State Space Least Mean Square Adaptive Filter for Power Line Interference removal from Cardiac Signals

Cardiac signals especially, electrocardiogram (ECG) becomes more vulnerable to external noises as compared to other biomedical signal and this is due to the non-invasive nature of ECG and the environment in which it is being recorded. Among other external noises, power line interference (PLI) is the most disquieting noise. This paper proposes an adaptive noise canceller (ANC) based on State Space Least Mean Square (SSLMS) for removal of PLI. Nevertheless, SSLMS is employed for adaptively tracking of amplitude, phase of known frequency of PLI sinusoids. Moreover, SSLMS is a model-dependent recursive algorithm whose convergence rate and tracking capability increase in instinctive manner when system's a priori knowledge is provided. Furthermore, the proposed algorithm does not require any reference, hence can effectively be applied for real time scenario. The purposed SSLMS adaptive filter performs effectually even when signal to noise ratio of contaminated ECG signal is low. The results show that proposed algorithm successfully removes PLI of 50 Hz frequency.