Novel Logarithmic Reference Free Adaptive Signal Enhancers for ECG Analysis of Wireless Cardiac Care Monitoring Systems

Abstract

In remote cardiac care monitoring applications, electrocardiogram (ECG) signals are contaminated by artifacts during data acquisition and transmission of signals. The removal of the artifacts is an important task for proper diagnosis. In this paper, an attempt has been made to remove the artifacts, especially baseline wander (BW), muscle artifacts (MA), power line interference (PLI), and electrode motion (EM) using a least mean logarithmic squares (LMLS) algorithm. Further to improve the filtering ability and speed up the convergence process, data normalization is applied. The above algorithm can be normalized with reference to maximum data normalization which leads to reduced computational complexity in the denominator. Based on the above algorithms, various adaptive signal enhancers (ASE's) are developed. To reduce the computational complexity of the signal enhancer, the proposed ASE's are combined with sign-based algorithms. The proposed ASE's are tested on real ECG signals obtained from the MIT-BIH database to compare the performance. The simulation results obtained illustrate that the block-based algorithms are better than LMLS in terms of the signal to noise ratio (SNR), excess mean square error, and computational complexity. Among the LMLS variants, the BB-SRNLMLS-based ASE's have better filtering ability with a reduced number of computations. The improvement of the SNR achieved in the process through the use of BB-SRNLMLS-based ASE's are calculated as 13.2945 dBs, 12.4589 dBs, 16.4289 dBs, and 13.6423 dBs, respectively, for BW, MA, PLI, and EM artifacts.

Keywords

• Adaptive signal enhancer
• artifacts
• computational complexity
• electrocardiogram
• remote health care