Design and implementation of efficient low complexity biomedical artifact canceller for nano devices

In the current day scenario, with the rapid development of communication technology remote health care monitoring becomes as an intense research area. In remote health care monitoring, the primary aim is to facilitate the doctor with high resolution biomedical data. In order to cancel various artifacts in clinical environment in this paper we propose some efficient adaptive noise cancellation techniques. To obtain low computational complexity we combine clipping the data or error with Least Mean Square (LMS) algorithm. This results sign regressor LMS (SRLMS), sign LMS (SLMS) and sign LMS (SSLMS) algorithms. Using these algorithms, we design Very-large-scale integration (VLSI) architectures of various Biomedical Noise Cancellers (BNCs). In addition, the filtering capabilities of the proposed implementations are measured using real biomedical signals. Among the various BNCs tested, SRLMS based BNC is found to be better with reference to convergence speed, filtering capability and computational complexity. The main advantage of this technique is it needs only one multiplication to compute next weight. In this manner SRLMS based BNC is independent of filter length with reference to its computations. Whereas, the average signal to noise ratio achieved in the noise cancellation experiments are recorded as 7.1059dBs, 7.1776dBs, 6.2795dBs and 5.8847dBs for various BNCs based on LMS, SRLMS, SLMS and SSSLMS algorithms respectively. Design and implementation of efficient low complexity biomedical artifact canceller for nano devices Md Zia Ur RAHMAN, Asiya SULTANA, Burra Venkata SRIKANTH