基于自适应卡尔曼滤波的生理电信号降噪方法

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中图分类号：TN911.7-34 文献标识码：A 文章编号：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号 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

人体的生理电信号，如心电信号（Electrocardiogram，ECG）、肌电信号（Electromyogram，EMG）等，能直接反映身体健康状况，因此被作为评估人体一些生理功能的重要参数，为远程医疗、实时监控、医学检测以及新兴的脑-机接口等领域提供了重要的研究基础[1-3]。（剩余7467字）