The Research Of Adaptive RLS Algorithm In ECG Signal Denoising

In the field of biomedical research, ECG signal detection and processing is of great significance. In practice, ECG signals inevitably exist various kinds of noise,which has great impact on ECG signal detection, sometimes even submergeds ECG signals, and seriously influences to the ECG waveform analysis and diagnosis.Therefore, research on a method how to effectively remove the noise in ECG signal has important practical significance.Adaptive filter theory is an important component in the field of statistical signal processing part. For the problem of time-varying signal processing of unknown environment, adaptive filter is better than the traditional fixed filter, and have the ability of low computational complexity and quick convergence rate. Adaptive filter is usually consists of two parts: the filter structure and the adaptive filter algorithm, and the most core part is the design of the adaptive algorithm.This paper mainly uses the software method to implement the noise elimination in the ECG signal, through the study of variou kinds of adaptive algorithms, and on the basis of comparing various algorithms performance, an algorithm which can effectively filter out the noise in ECG signal is established. Firstly, analyze the minimum mean square rule of LMS algorithm, the normalized LMS algorithm,variable step size LMS algorithm and least squares criterion of the recursive least squares of RLS algorithm in detail. Through the analysis of these algorithms, it is found that RLS algorithm can better adaptively track the outside noise interference whos statistical properties change, and have faster convergence speed and better stability than LMS algorithm. Finally, this paper selects the RLS algorithm adaptive noise cancellation system to deal with ECG signal, and the simulation results show that this algorithm not only can quickly and effectively remove noise so that filtering effect is remarkable, but also can effectively improve the ECG waveform and improve the accuracy of ECG analysis and diagnosis.