Research On Application Of Adaptive Filtering Algorithms In ECG Monitoring

An efficient algorithm for abstract ECG signals from strong-noise ispresented in this research. The algorithm was proved to be fast and highefficient. It has important meaning in ECG monitoring.ECG signal is one of the most important electrophysiological parameters.ECG signal is non-stationary as the environment and detecting conditionchanged. The detecting and processing of ECG signal has very importantmeaning in research on bio-medical engineering signal-processing. The curverecords ECG signal intitules ECG. The channels transfer ECG signal intitulesleads. ECG signal is a vector, so ECG gotten from different positions hasdifferent characters. Mostly ECG has a P-wave ,a QRS-complex and a T-wave,sometimes a little U-wave appear after T-wave. ECG as the record of heartactivity plays a decisive role in diagnoses of heart disease.ECG monitoring is to analyse ECG and diagnose the patient throughreal-time inspecting or review the ECG. ECG means very import to heartdisease patient and medical research. The first task of ECG monitoring is todetect the figure of ECG.Noises enter ECG as the environment and the patient changed. Noises willinfect the autoanalysis function of the ECG monitor. Noises in ECG can mainlypartition into four kinds: power frequency interference (50/60Hz),electorphysiological signals except ECG signal, baseline drift and strong noise.Power frequency interference is produced by lighting and power impetus.Electrophysiological except mainly means electromyography(EMG) and ECGof fetus. Baseline drift is produced by body move, breath and bad electrodecontract. Electrosurgical unit (ESU) and large electrical equipment etc cangenerate strong noise. Strong noise has wide frequency bands and highamplitude and it often submerge ECG signal. There is no good method toremove strong noises presently neither software nor hardware, so research aneffective method to restrain strong noise is important and necessary for ECGmonitoring. The aim of the research is to find a method to remove strong noise fromECG based on multiple leads calculator and adaptive noise eliminator. In actualapplication, we don't know the statistical characteristics of mostly randomsignals. Adaptive noise eliminator is used to resolve the problem. Adaptivenoise eliminator has two inputs. The base input comprises useful signal andnoise. The referenced input was noise correlated with the noise in base input. Inactual research , ECG including strong noise is the base input and the noisecalculated from multiple leads is the reference input. The research has three key techniques. (1) Choice of leads. The leads wechosen should ensure the referenced input to be correlated with the noise inbasic input. (2) Setting of weighted vector initializing. The weighted vector willgradually converge at aptotic value. We initialize some special value onweighted vector based on statistical data. It will accelerate the processing ofconvergence. (3) Adjusting of step size. Step is restricted by formula:0 < μ< 2 . In filtering we forecast the power spectral of next segment to MSmaxadjust step size to meet the adaptive process convergence. The innovation of research is that we filter the strong noise from ECGwithout any additional equipment.