Robust Design of Nonlinear Adaptive Hammerstein Filter Structure Using Evolutionary Algorithm: Real-Time Application...

Electrocardiogram (ECG) signals are well-known non-stationary heart signals of lower strength. Due to their small amplitude, it attracts other biomedical artefacts from the surrounding. This research mainly focuses on removing artefacts from the electrocardiogram signals.

The work presented uses the recent metaheuristic techniques to design a nonlinear adaptive Hammerstein filter-based structure efficiently. Many powerful metaheuristic optimisation algorithms, such as particle swarm optimisation algorithm with constriction factor, flower pollination algorithm, marine predators' algorithm and growth optimiser, have been applied for the optimal design of adaptive Hammerstein filter-based structures. The proposed structure has been analysed for electrocardiogram with various noise signals such as muscle artefact, white Gaussian noise etc. RESULTS: Among the adopted-metaheuristic algorithms applied to adaptive Hammerstein filter-based structures, the growth optimiser-optimised adaptive Hammerstein filter-based structures performed better with improved signal-to-noise ratio and minimal mean squared error values. A digital signal processor kit is used to authenticate the simulation outcomes.

The results (mean squared error: 3.698E-08 and signal-to-noise ratio improvement: 12 dB) obtained through the proposed technique ensure its supremacy compared to other state-of-the-art techniques.

Hence, the proposed method can be utilised for electrocardiogram signal enhancement.