Abstract
Amid various computational techniques, wavelet technique has taken universal place in biomedical signal processing area. Wavelet transform (WT) is an effective method for analysis of adaptable signals where both time and frequency information are vital. The most important role of wavelet transform is the elimination of noise from the biomedical signal. Analysis of ECG signals using computational intelligence techniques like discrete wavelet transform (DWT), principle component analysis (PCA), and independent component analysis (ICA) is presented vividly in this paper. A new modified wavelet transform called bionic wavelet transform (BWT) has been applied here for analysis of biomedical signals. By adapting the value of scales, T-function of BWT is varied and its effects on the value of the threshold is observed. This is called the multi-adaptive technique which is used for denoising purpose of electrocardiogram signal (ECG). The efficiency of various methods used by existing techniques for denoising of ECG signals has been evaluated and compared in terms of signal-to-noise ratio (SNR). From the proposed algorithm, i.e., with BWT, it is observed that there is an improvement in SNR than other conventional techniques.
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Appendix
Appendix
WT technique: \( a = {a_0}^m \), \( b = n{b_0}{a_0}^m \), \( \sigma \)=MAD/0.6745; BWT technique: \( \tilde{G}_{1} = 0.87 \) and \( \tilde{G}_{2} = 45 \), \( BWT_{s} = 0.8 \); Simulink model: \( e^{{t^{2} }} = 1.7725 \).
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Ray, P., Mandal, K.K., Mohanty, B.K. (2019). Analysis of Electrocardiogram Signal Using Computational Intelligence Technique. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_49
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DOI: https://doi.org/10.1007/978-981-13-1819-1_49
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