ARTIFACT REMOVAL IN ECG SIGNALS USING MODIFIED DATA NORMALIZATION BASED SIGNAL ENHANCEMENT UNITS FOR HEALTH CARE MONITORING SYSTEMS 1

Low complexity noise cancellation structures are needed for reliable transmission of ECG signals at real time environments. These low complexity structures can be developed with the help of the partial update techniques for better convergence and complexity. In this paper the same idea is used to derive several structures which are good at convergence and complexity. Based on partial update mechanism of the coefficients of the adaptive filter, we upgraded the conventional normalized least mean square (NLMS) algorithm. This modified algorithm updates only some coefficients of the taps where the signal characteristics widely deviate from the previous iteration. The modified NLMS (MNLMS) based on partial update mechanism is combined with signum based algorithms to minimize number of multiplications during filtering process. Further, we proposed maximum value of data for normalizing the step size to decrease the number of multiplications in the denominator of the normalization function. These are suitable to operate at high data rate applications, and to test the working of these structures in real time conditions the MIT-BIH arrhythmia database was used. Here the signal to noise ratio, the miss adjustment error is used as performance measures and all the test data is tabulated. The structures have shown good performance over the standard LMS algorithm in terms of the filtering, complexity and convergence.

[1]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[2]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[3]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[4]  Carsten Meyer,et al.  Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals , 2006, IEEE Transactions on Information Technology in Biomedicine.

[5]  M. Z. U. Rahman,et al.  Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring , 2012, IEEE Sensors Journal.

[6]  Alireza K. Ziarani,et al.  A nonlinear adaptive method of elimination of power line interference in ECG signals , 2002, IEEE Transactions on Biomedical Engineering.

[7]  Roger Abächerli,et al.  A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Shuenn-Yuh Lee,et al.  Low-Power Wireless ECG Acquisition and Classification System for Body Sensor Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[9]  Pablo Laguna,et al.  Steady-state MSE convergence of LMS adaptive filters with deterministic reference inputs with applications to biomedical signals , 2000, IEEE Trans. Signal Process..

[10]  Jeroen J. Bax,et al.  The significance of stress-induced ST segment depression in patients with inferior Q wave myocardial infarction. , 1999, Journal of the American College of Cardiology.

[11]  Y. Yasuda,et al.  Filtering noncorrelated noise in impedance cardiography , 1995, IEEE Transactions on Biomedical Engineering.

[12]  J. Dekker,et al.  ST segment and T wave characteristics as indicators of coronary heart disease risk: the Zutphen Study. , 1995, Journal of the American College of Cardiology.

[13]  Rafi Ahamed Shaik,et al.  Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry , 2011, Signal Process..

[14]  Refet Firat Yazicioglu,et al.  A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP , 2015, IEEE Journal of Solid-State Circuits.

[15]  Woon-Seng Gan,et al.  A lowcomplexity fast converging partial update adaptive algorithm employing variable step-size for acoustic echo cancellation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  J.M. Ansermino,et al.  A Wavelet Approach to Detecting Electrocautery Noise in the ECG , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[17]  Aime Lay-Ekuakille,et al.  An efficient cardiac signal enhancement using time–frequency realization of leaky adaptive noise cancelers for remote health monitoring systems , 2013 .

[18]  Marian Kotas,et al.  Application of projection pursuit based robust principal component analysis to ECG enhancement , 2006, Biomed. Signal Process. Control..