A Computationally Efficient Distributed Framework for a State Space Adaptive Filter for the Removal of PLI from Cardiac Signals

The proliferation of cardiac signals, such as high-resolution electrocardiograms (HRECGs), ultra-high-frequency ECGs (UHF–ECGs), and intracardiac electrograms (IEGMs) assist cardiologists in the prognosis of critical cardiac diseases. However, the accuracies of such diagnoses depend on the signal qualities, which are often corrupted by artifacts, such as the power line interference (PLI) and its harmonics. Therefore, state space adaptive filters are applied for the effective removal of PLI and its harmonics. Moreover, the state space adaptive filter does not require any reference signal for the extraction of desired cardiac signals from the observed noisy signal. Nevertheless, the state space adaptive filter inherits high computational complexity; therefore, filtration of the increased number of PLI harmonics bestows an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed framework for the state space least mean square with adoptive memory (PD–SSLMSWAM) is introduced, which runs the computationally expensive SSLMSWAM adaptive filter parallelly. The proposed architecture efficiently removes the PLI along with its harmonics even if the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSLMSWAM scheme provides less computational costs as compared to the sequentially operated SSLMSWAM algorithm. A comparison was drawn among the proposed PD–SSLMSWAM, sequentially operated SSLMSWAM, and state space normalized least mean square (SSNLMS) adaptive filters in terms of qualitative and quantitative performances. The simulation results show that the proposed PD–SSLMSWAM architecture provides almost the same qualitative and quantitative performances as those of the sequentially operated SSLMSWAM algorithm with less computational costs. Moreover, the proposed PD–SSLMSWAM achieves better qualitative and quantitative performances as compared to the SSNLMS adaptive filter.