基于自適應(yīng)卡爾曼濾波的生理電信號降噪方法

打開文本圖片集

中圖分類號：TN911.7-34 文獻標(biāo)識碼：A 文章編號：1004-373X（2025）10-0039-06

Abstract：Duringthemeasurementof physiologicalelectricalsignals，thetargetsignalisoftendisturbedbyvariousnoises， includingexteralelectromagneticfieldinterferenceandotherinternalphysiologicalelectricalsignalinterference，themost seriousof whichispowerfrequencyinterference.Thesenoiseinterferenceswillbringgreatinconvenience totheanalysisand processingof physiologicalelectricalsignals.Therefore，anoisereductionmethodbasedonadaptiveKalmanflterisproposedto eliminate thenoiseinterferencesuchaspowerfrequencymixedinphysiological electrical signals.Theadvantagesofadaptive filteringindynamicweightadjustmentandtheacuracyofKalmanfiteringinstateestimationarefullyused toaccurately identifyand processtargetsignalsandnoises.Theelectrocardiogram （ECG），theelectroculogram（EOG）andtheelectromogram （EMG）collctedintheordinary experimentalenvironmentareprocessed，andthetimedomainwaveformandspectrumbeforend afterthealgoritproessingiservd，soastotestfoeectivenesoftedaptiveKalmanfierinteoiseduction ofphysiologicalelectrical signal.TheresultsshowthatthedesignedadaptiveKalman filtercanefectivelyeliminatenoise interferencesuchaspowerfrequency（includingfundamentalfrequencyandharmoniccomponents），makethetargetsignalclearer andcleaner，anddonotdamage theusefulcomponentsofthetarget signal.Theaverage decreaseof thespectralvalueat 50Hz （204號 is notlessthan49.31dB.TheadaptiveKalmanalgorithmcanbeapliedtoavarietyofdiferentphysiologicalelectricalsignals onlybyadjustingsome parameters，whichcaneffectivelyfilteroutthepowerfrequencyandother noiseinterferencemixed inthe originalsignal.Thenoisereductionperformanceisstableandthecomputationalcomplexityislow，whichprovidesamore effective solution for the analysis and processing of physiological electrical signals.

Keywords：adaptivefilter;Kalmanfilter;physiologicallectricalsignal;EOG;powerfrequencyinterference;noiseduction

0 引言

人體的生理電信號，如心電信號（Electrocardiogram，ECG）、肌電信號（Electromyogram，EMG）等，能直接反映身體健康狀況，因此被作為評估人體一些生理功能的重要參數(shù)，為遠程醫(yī)療、實時監(jiān)控、醫(yī)學(xué)檢測以及新興的腦-機接口等領(lǐng)域提供了重要的研究基礎(chǔ)[1-3]。（剩余7467字）