ClassifyTimeSeriesUsingWaveletAnalysisandDeepLearning

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Thisexampleshowshowtoclassifyhumanelectrocardiogram(ECG)signalsusingthecontinuouswavelettransform(CWT)andadeepconvolutionalneuralnetwork(CNN).

TrainingadeepCNNfromscratchiscomputationallyexpensiveandrequiresalargeamountoftrainingdata.Invariousapplications,asufficientamountoftrainingdataisnotavailable,andsynthesizingnewrealistictrainingexamplesisnotfeasible.Inthesecases,leveragingexistingneuralnetworksthathavebeentrainedonlargedatasetsforconceptuallysimilartasksisdesirable.Thisleveragingofexistingneuralnetworksiscalledtransferlearning.InthisexampleweadapttwodeepCNNs,GoogLeNetandSqueezeNet,pretrainedforimagerecognitiontoclassifyECGwaveformsbasedonatime-frequencyrepresentation.

AfterdownloadingthedatafromGitHub,unzipthefileinyourtemporarydirectory.

UnzipECGData.zipinphysionet-ECG_data-main.LoadthedatafileintoyourMATLABworkspace.

helperPlotReps(ECGData)

Aftermakingthefolders,createtime-frequencyrepresentationsoftheECGsignals.Theserepresentationsarecalledscalograms.AscalogramistheabsolutevalueoftheCWTcoefficientsofasignal.

Tocreatethescalograms,precomputeaCWTfilterbank.PrecomputingtheCWTfilterbankisthepreferredmethodwhenobtainingtheCWTofmanysignalsusingthesameparameters.

LoadthepretrainedGoogLeNetneuralnetwork.IfDeepLearningToolboxModelforGoogLeNetNetworksupportpackageisnotinstalled,thesoftwareprovidesalinktotherequiredsupportpackageintheAdd-OnExplorer.Toinstallthesupportpackage,clickthelink,andthenclickInstall.

InspectthefirstelementofthenetworkLayersproperty.ConfirmthatGoogLeNetrequiresRGBimagesofsize224-by-224-by-3.

Eachlayerinthenetworkarchitecturecanbeconsideredafilter.Theearlierlayersidentifymorecommonfeaturesofimages,suchasblobs,edges,andcolors.Subsequentlayersfocusonmorespecificfeaturesinordertodifferentiatecategories.GoogLeNetispretrainedtoclassifyimagesinto1000objectcategories.YoumustretrainGoogLeNetforourECGclassificationproblem.

Inspectthelastfourlayersofthenetwork.

Replacethefullyconnectedlayerloss3-classifierwithanewfullyconnectedlayerwiththenumberoffiltersequaltothenumberofclasses.Tolearnfasterinthenewlayersthaninthetransferredlayers,increasethelearningratefactorsofthefullyconnectedlayer.

Traininganeuralnetworkisaniterativeprocessthatinvolvesminimizingalossfunction.Tominimizethelossfunction,agradientdescentalgorithmisused.Ineachiteration,thegradientofthelossfunctionisevaluatedandthedescentalgorithmweightsareupdated.

Trainingcanbetunedbysettingvariousoptions.InitialLearnRatespecifiestheinitialstepsizeinthedirectionofthenegativegradientofthelossfunction.MiniBatchSizespecifieshowlargeofasubsetofthetrainingsettouseineachiteration.Oneepochisafullpassofthetrainingalgorithmovertheentiretrainingset.MaxEpochsspecifiesthemaximumnumberofepochstousefortraining.Choosingtherightnumberofepochsisnotatrivialtask.Decreasingthenumberofepochshastheeffectofunderfittingthemodel,andincreasingthenumberofepochsresultsinoverfitting.

EvaluateGoogLeNetAccuracy

Evaluatethenetworkusingthevalidationdata.

ExploreGoogLeNetActivations

EachlayerofaCNNproducesaresponse,oractivation,toaninputimage.However,thereareonlyafewlayerswithinaCNNthataresuitableforimagefeatureextraction.Inspectthefirstfivelayersofthetrainednetwork.

ExaminewhichareasintheconvolutionallayersactivateonanimagefromtheARRclass.Comparewiththecorrespondingareasintheoriginalimage.Eachlayerofaconvolutionalneuralnetworkconsistsofmany2-Darrayscalledchannels.Passtheimagethroughthenetworkandexaminetheoutputactivationsofthefirstconvolutionallayer,conv1-7x7_s2.

Findthestrongestchannelforthisimage.Comparethestrongestchannelwiththeoriginalimage.

SqueezeNetisadeepCNNwhosearchitecturesupportsimagesofsize227-by-227-by-3.EventhoughtheimagedimensionsaredifferentforGoogLeNet,youdonothavetogeneratenewRGBimagesattheSqueezeNetdimensions.YoucanusetheoriginalRGBimages.

Load

LoadthepretrainedSqueezeNetneuralnetwork.IfDeepLearningToolboxModelforSqueezeNetNetworksupportpackageisnotinstalled,thesoftwareprovidesalinktotherequiredsupportpackageintheAdd-OnExplorer.Toinstallthesupportpackage,clickthelink,andthenclickInstall.

ToretrainSqueezeNettoclassifynewimages,makechangessimilartothosemadeforGoogLeNet.

Inspectthelastfivenetworklayers.

augimgsTrain=augmentedImageDatastore([227227],imgsTrain);augimgsValidation=augmentedImageDatastore([227227],imgsValidation);SetTrainingOptionsandTrainSqueezeNet

CreateanewsetoftrainingoptionstousewithSqueezeNet,andtrainthenetwork.

EvaluateSqueezeNetAccuracy

helperCreateECGDataDirectoriescreatesadatadirectoryinsideaparentdirectory,thencreatesthreesubdirectoriesinsidethedatadirectory.ThesubdirectoriesarenamedaftereachclassofECGsignalfoundinECGData.

functionhelperCreateECGDirectories(ECGData,parentFolder,dataFolder)%ThisfunctionisonlyintendedtosupporttheECGAndDeepLearningExample.%Itmaychangeorberemovedinafuturerelease.rootFolder=parentFolder;localFolder=dataFolder;mkdir(fullfile(rootFolder,localFolder))folderLabels=unique(ECGData.Labels);fori=1:numel(folderLabels)mkdir(fullfile(rootFolder,localFolder,char(folderLabels(i))));endendhelperPlotRepsplotsthefirstthousandsamplesofarepresentativeofeachclassofECGsignalfoundinECGData.

HowtoGetBestSitePerformance

SelecttheChinasite(inChineseorEnglish)forbestsiteperformance.OtherMathWorkscountrysitesarenotoptimizedforvisitsfromyourlocation.

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ClassifyTimeSeriesUsingWaveletAnalysisandDeepLearning

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