Abstract
Electrocardiogram (ECG) signal helps the physicians in the detection of cardiac-related diseases. Many noises like power line interference (PLI), baseline wander, electromyography (EMG) noise and burst noise are contaminated with the raw signal and corrupt the shape of the waveform which makes the detection faulty. So in recent years, many signal processing methods are proposed for removal of these noise artifacts effectively. In this paper, two hybrid methods, i.e., empirical mode decomposition (EMD) with wavelet transform filtering and EMD with non-local means (NLM) are proposed. The results are analyzed with performance parameters like signal to noise ratio (SNR), mean square error (MSE), and percent root mean square difference (PRD). The results exhibit better performance in hybrid method of EMD with NLM technique.
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Garnaik, S., Rout, N.C., Sethi, K. (2019). Noise Reduction in Electrocardiogram Signal Using Hybrid Methods of Empirical Mode Decomposition with Wavelet Transform and Non-local Means Algorithm. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_57
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DOI: https://doi.org/10.1007/978-981-10-8055-5_57
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