Abstract
The ability to extract high resolution and valid ECG signals from contaminated recordings is an important subject in the biotelemetry systems. During ECG acquisition several artefacts strongly degrades the signal quality. The dominant artefacts encountered in ECG signal such as Power Line Interference, Muscle Artefacts, Baseline Wander, Electrode Motion Artefacts; and channel noise generated during transmission. The tiny features of ECG signal are masked due to these noises. To track random variations in noisy signals, the adaptive filter is used. In this paper, we proposed Dead Zone Leaky Least Mean Square algorithm, Leaky Least Mean Froth algorithm and Median Leaky LMS algorithms to remove PLI and EM artefacts from ECG signals. Based on these algorithms, we derived some sign based algorithms for less computational complexity. We compare the proposed algorithms with LMS algorithm, which shows better performance in weight drift problem, round off error and low steady state error. The simulation results show that Dead Zone Leaky LMS algorithm gives good correlation factor and SNR ratio.
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Gowri, T., Rajesh Kumar, P., Koti Reddy, D.V.R., Rahman, U.Z. (2016). Removal of Artifacts from Electrocardiogram Using Efficient Dead Zone Leaky LMS Adaptive Algorithm. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_64
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DOI: https://doi.org/10.1007/978-81-322-2538-6_64
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