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The ElectroCardioGram (ECG) signal is a graphical representation of the human heart activity. The acquired ECG signal is interfered with different artefacts. Power Line Interference (PLI) is the main source of noise to affect the ECG signal. Adaptive filtering techniques like Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Error Nonlinearity Least Mean Square (ENLMS) are used to remove the noise from ECG signal. The three algorithms are developed and the performance the three algorithms are analyzed. Among the three algorithms, ENLMS algorithm effectively removes the PLI from…mehr

Produktbeschreibung
The ElectroCardioGram (ECG) signal is a graphical representation of the human heart activity. The acquired ECG signal is interfered with different artefacts. Power Line Interference (PLI) is the main source of noise to affect the ECG signal. Adaptive filtering techniques like Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Error Nonlinearity Least Mean Square (ENLMS) are used to remove the noise from ECG signal. The three algorithms are developed and the performance the three algorithms are analyzed. Among the three algorithms, ENLMS algorithm effectively removes the PLI from ECG signals and gives better results compared to LMS and NLMS.
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Autorenporträt
Le Dr. Chandra Mohan Reddy Sivappagari travaille en tant que professeur associé à l'Université technologique Jawaharlal Nehru d'Anantapur (JNTUA), Ananthapuramu, Andhra Pradesh, Inde. Il a obtenu son doctorat en ingénierie à la JNTUA en 2014. Ses principaux domaines de recherche sont le traitement des signaux pour les communications et l'Internet des objets.