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Sensors 2017, 17(5), 942;

Adaptive Interference Cancellation of ECG Signals

School of Electronic Engineering, Xidian University, Xi’an 710071, China
King Saud University, Riyadh 11653, Saudi Arabia
National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Department of Computer Sciences, Florida Institute of Technology (FIT), Melbourne, FL 32901, USA
Author to whom correspondence should be addressed.
Academic Editors: Giancarlo Fortino, Hassan Ghasemzadeh, Wenfeng Li, Yin Zhang and Luca Benini
Received: 11 March 2017 / Revised: 9 April 2017 / Accepted: 15 April 2017 / Published: 25 April 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts. View Full-Text
Keywords: ECG signal; interference cancellation; LMS algorithm ECG signal; interference cancellation; LMS algorithm

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Ren, A.; Du, Z.; Li, J.; Hu, F.; Yang, X.; Abbas, H. Adaptive Interference Cancellation of ECG Signals. Sensors 2017, 17, 942.

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