Abstract
About 15 million people alive today have been influenced by coronary illness. This is a major and critical issue in recent days. There are so many people have been lost their lives due to heart attack and other heart related issues. So, early on analysis and proper cure of heart disease is required to minimize the death rate due to heart disease. For better diagnosis we need exact and consistent tools for determine the fitness of human hearts to analysis the disease ahead of time before it makes around an undesirable changes in human body. For heart diagnosis one of the tools is Electrocardiogram (ECG) and the obtained signal is labeled ECG signal. This ECG signal contaminated by an amount of motion artifacts and noisy elements and deduction of these noisy elements from ECG signal must important before the ECG signal could be utilized for illness diagnosis purpose. There are various filter methods available for denoising ECG signal and select the best one on the dependence of performance parameter like signal to noise ratio (SNR) and power spectrum density (PSD).
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References
Rik Vullings: An Adaptive Kalman Filter for ECG signal Enhancement. IEEE transactions on biomedical engineering, vol. 58, no. 4, pp. 1094–1103 (2011).
Mahesh S. Chavan, RA. Agrawal, M.D. Uplane: Suppression of Baseline Wander and Power line interfacing in ECG using Digital IIR filter. International journal of circuits, systems and signal processing, vol. 2 issue 2, pp. 356–365 (2008).
Priya Krishnamurthy, N. Swethaanjali, M. Arthi Bala Laxshmi: Comparison of Various Filtering Techniques Used For Removing High Frequency Noise in ECG Signal. International journal of students research in technology & management, Vol. 3 no. 1, pp. 211–215 (2015).
Nidhi Rastogi, Rajesh Mehra: Analysis of Butterworth and Chebyshev Filters for ECG Denoising Using Wavelets. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 6, issue 6, pp. 37–44, (2013).
Lukas Smital: Adaptive Wavelet Weiner Filtering of ECG signal. IEEE transactions on biomedical engineering, vol. 60 no. 2, pp. 437–445 (2013).
Omid Sayadi: ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure. IEEE transaction on biomedical engineering, vol. 55 no. 9, pp. 2240–2248 (2008).
Kohler, B.-U, Henning, C., Orglmeister, R.: The Principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine, vol. 21 issue 1, pp. 42–57 (2002).
Mohan M. Kumar: Simulating Motion Artifacts in ECG using Wiener and Adaptive Filter structure. International journal of emerging technology and advanced engineering, vol. 2 issue 2, pp. 237–260 (2012).
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Sharma, B., Jenkin Suji, R., Basu, A. (2018). Adaptive Kalman Filter Approach and Butterworth Filter Technique for ECG Signal Enhancement. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-3920-1_32
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DOI: https://doi.org/10.1007/978-981-10-3920-1_32
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