Design of efficient Wiener spline adaptive filter for electrocardiogram signal enrichment

Electrocardiogram signal enrichment has become a searing topic in recent research. This paper deals with designing an optimal Wiener spline adaptive filter (WSAF) for noise removal from corrupted ECG signals. Two time-tested, benchmark metaheuristic algorithms along with a recently emerged one, namely particle swarm optimisation, teaching–learning based optimisation and honey badger optimiser algorithm, respectively, have been adopted to optimise the weights of WSAF and the output obtained are comparatively analysed with reported methods. The proposed WSAF outperforms visual and mathematical analysis with an SNR of 94.54 dB and MSE of 1.71 × 10⁻¹¹. Further, the optimised WSAF output is implemented in hardware in processor kit TMS320C6713 DSK and is compared with the original ECG signal. The Proposed WSAF effectively extracts the noise and can be applied to other biomedical signals for artifact reduction.