Adaptive Signal Enhancement Unit for EEG Analysis in Remote Patient Care Monitoring Systems

In this paper we propose an efficient process of physiological artifact elimination methodology from brain waves (BW), which are also commonly known as electroencephalogram (EEG) signal. In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component. This leads to inaccurate and ambiguous diagnosis. As the statistical nature of the EEG signal is more non-stationery, adaptive filtering is the more promising method for the process of artifact elimination. In clinical conditions, the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used. This causes delay in diagnosis and decision making. To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error. The resultant adaptive algorithm based on this strategy is Non-linear Least mean square (NL2MS) algorithm. Again, to improve this algorithm in terms of filtering capability we perform data normalization, using this algorithm several hybrid versions are developed to improve filtering and reduce computational operations. Using the method, a new signal enhancement unit (SEU) is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject. The ability of the proposed schemes is measured in terms of convergence, enhancement and multiplications required. Among various SEUs, the MCN2L2MS algorithm achieves 14.6734, 12.8732, 10.9257, 15.7790 dB during the artifact removal of RA, EMG, CSA and EBA components with only two multiplications. Hence, this algorithm seems to be better candidate for artifact elimination.