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10/17/19 1 Artificial Intelligence and Sleep – The Next Frontier Nathaniel F. Watson, MD, MSc Professor of Neurology University of Washington (UW) School of Medicine Director, Harborview Medical Center Sleep Clinic Co-director, UW Medicine Sleep Center @SleepDocWatson Full Disclosure: No Relation/No COI ≠ University of Washington Watson

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Page 1: Artificial Intelligence and Sleep – The Next Frontiersasmhq.org/wp-content/uploads/2019/10/004_SASM_19... · •Machine learning models were trained, optimized, and evaluated: Ordinary

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1

ArtificialIntelligenceandSleep–TheNextFrontier

NathanielF.Watson,MD,MScProfessorofNeurology

UniversityofWashington(UW)SchoolofMedicineDirector,HarborviewMedicalCenterSleepClinic

Co-director,UWMedicineSleepCenter@SleepDocWatson

FullDisclosure:NoRelation/NoCOI

≠

UniversityofWashingtonWatson

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WhatIwilltellyou

• WhatisArtificialIntelligence(AI)?• SleepmedicineandAI• Readingtealeaves– howcan/willAItransformsleepmedicine?• WhatdoesAImeanforusmoregenerallyinmedicine/life?

• Howtofeelaboutit(butIWILLchallengeyoutothinkaboutit)

WhatIwon’ttellyou

InfiniteMonkeyTheorem

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WhatdowemeanbyArtificialIntelligence?• ArtificialIntelligence• Abranchofcomputersciencedealingwiththesimulationofintelligentbehaviorincomputers

• Thecapabilityofamachinetoimitateintelligenthumanbehavior• MachineLearning• Theprocessbywhichacomputerisabletoimproveitsownperformance(asinanalyzingimagefiles)bycontinuouslyincorporatingnewdataintoanexistingstatisticalmodel

• ComputationalPhenotyping• Abiomedicalinformaticsmethodforidentifyingcertainpatientpopulations

MerriamWebster;https://www.coursera.org/lecture/computational-phenotyping/introduction-to-computational-phenotyping-s0GJ8

Waveforms = Trillions of data points

6

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PSGandtheDynamicMultivariateHumanPhysiologicalState

• Eachofthe3.2billionDNAbasepairsinahumangenomecanbeencodedbytwobits– 800megabytesfortheentiregenome• SequenceofnucleotidescomprisingDNAisrelativelystatic…whileenvironmentwithineachcellishighlyvariable• Genomesequencedoesnotindicateexposuretotoxicwater,howbadlyinjuredinafall,howarecentsurgeryorchangeinmedicationaffectedhealth,etc.• Bysomeestimates,yourphysiologicalstateatanypointintimecontainsroughly1018 (amilliontrillion)timesmoreinformationthanresidesinyourgeneticcode

CourtesyofChrisFernandez

StatisticalAnalysisofIndividualVersusConsensusScoringAgreement

Fernandezetal.Acrossvalidationapproachtointer-scorerreliabilityassessment.SLEEP2018;41:A122-123.

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Machine LearningAlgorithmforAutomatedScoringandAnalysisofPolysomnographyData

Allocca etal.Validationof‘Somnivore’,aMachineLearningAlgorithmforAutomatedScoringandAnalysisofPolysomnographyData.FrontiersinNeuroscience2019;13:1-18

DeepNeuralNetworkSleepScoring

Biswal etal.Expert-levelsleepscoringwithdeepneuralnetworks.JournaloftheAmericanMedicalInformaticsAssociation,25(12),2018,1643–1650

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CanAIEstimateOSASeveritybyEEGAlone?

• Adultpatients(N=4,650)whocompletedanovernightPSGstudy• Allsignalswereexcludedfromanalysisexceptthe10/20EEGsensorarray• GlobalphenotypicfeatureswerederivedfromEEGstudysleeparchitectureandfragmentationprofiles• Localphenotypicfeatureswerederivedbyanalyzingbiomarkerpatternsandrespiratorycycle-relatedEEGchangesexhibitedintheEEGsignalsdirectly• AImethodsincludingBidirectional-LSTM,Deep-CNN,andacombinationofbothweretrained,optimized,andevaluatedtomodeltherelationshipbetweenglobalandlocalEEGphenotypesandOSAseverity• PerformanceforpredictingmoderateandsevereOSA(AHI≥15)wasevaluatedusingrandomized10-foldcross-validation

Fernandezetal.UsingnovelEEGphenotypesandAItoestimateOSAseverity.SLEEP2019;42:A375

Sleep-ArousalArchitectureEstimatedLow-RiskforOSA(AHI≤15)

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Sleep-ArousalArchitectureEstimatedHigh-RiskOSA(AHI≥15)

PerformanceandStatisticalSignificanceofEEGbasedOSASeverityEstimation

https://towardsdatascience.com/precision-vs-recall-386cf9f89488

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MachineLearningPredictsCPAPRxPressure± 2cmH2OCPAPStartingPressureComparison

PhysicianRxCPAPPressure

Machine

LearningPredicted

Th

erapeu

ticCPA

PPressure

Munafo etal.ComputationalphenotypinginCPAPtherapy:Usinginterpretablephysiology-basedmachinelearningmodelstopredicttherapeuticCPAPpressures.SLEEP2019;42:A217.

MachineLearningBasedPAPComplianceAssessment:

• Snoringtime• Heartrate• Longestapnea• ESSscore• PercenttimeunderSpO2 of85%• Numberofapneas/hour

RandomForestAnalysis:5-yearAllCauseMortality• 1,541interpretablephysiologicalandclinicalfeaturescomputationallyderivedfromtheSHHSdataset(N=5,803)• 435clinicalobservationalvariables(e.g.,smoking,bloodpressure,cholesterol)

• 1,170PSGvariables(e.g.,sleeparchitecture,AHI,SpO2 trends)• Compumedics P-seriestypeIIPSG• Machinelearningmodelsweretrained,optimized,andevaluated:OrdinaryLeastSquares,RandomForest,DeepMLP,KernelSVM,NaïveBayes,KNN,GaussianProcess,QDA,LASSO,LogisticRegression,AdaBoost

Fernandezetal.ComputationalphenotypinginPSG:Usinginterpretablephysiology-basedmachinelearningmodelstopredicthealthoutcomes.SLEEP2017;40:A26.

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PredictiveUtilityRanking

TableofTop-30PSGvariableandclinicalobservationfeaturesrankedbyGiniImportance:

PSG-only,Obs-only,andCombinedRandomForestAnalysis

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ComputationalPhenotypingModelComparison

Fernandezetal.ComputationalphenotypinginPSG:Usinginterpretablephysiology-basedmachinelearningmodelstopredicthealthoutcomes.SLEEP2017;40:A26.

Zinchuketal.Phenotypesinobstructivesleepapnea:Adefinition,examplesandevolutionofapproaches.SleepMedicineReviews35(2017)113-123

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Can“BrainAge”BePredictedbytheEEG?

• Brainage(BA)servesasapotentialagingbiomarkerwherethevariationofBAbetweenindividualsofthesamechronologicalagemaycarryimportantinformationabouttheriskofcognitiveimpairment,neurologicalorpsychiatricdisease,ordeath• Alzheimer’sdisease,schizophrenia,epilepsy,traumaticbraininjury,bipolardisorder,majordepression,cognitiveimpairment,diabetesmellitus,andHIV,areassociatedwithexcessBA(onMRI)• MachinelearningmodeldevelopedtopredictBAbasedon2largesleepEEGdatasets:theMassachusettsGeneralHospital(MGH)sleeplabdataset(N=2,532;ages18-80);andtheSleepHeartHealthStudy(SHHS,N=1,974;ages40-80).

Sunetal.BrainagefromEEGofsleep.NeurobiologyofAging2019;74:112-120

Sunetal.BrainagefromEEGofsleep.NeurobiologyofAging2019;74:112-120

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Sunetal.BrainagefromEEGofsleep.NeurobiologyofAging2019;74:112-120

Sunetal.BrainagefromEEGofsleep.NeurobiologyofAging2019;74:112-120

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TimetoThinkAboutit

•Whatdoesitmean:• Forsleepmedicine?• Formedicineingeneral?• Forusasmembersofsociety?

ProtecttheSleepMedicineStatusQuo

EmbracetheFuture

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• Genotype - thegeneticconstitutionofanindividualorganism• Phenotype - thesetofobservablecharacteristicsofanindividualresultingfromtheinteractionofitsgenotypewiththeenvironment• Geneticdeterminism- thebeliefthathumanbehavioriscontrolledbyanindividual's genes orsomecomponentoftheirphysiology,generallyattheexpenseoftheroleoftheenvironment,whetherinembryonicdevelopmentorinlearning• Thequantifiedself- refersbothtothe culturalphenomenonofself-trackingwithtechnologyandtoacommunityofusersandmakersofself-trackingtoolswhoshareaninterestin“self-knowledgethroughnumbers.”• Phenotypedeterminism- ???

ComparisonofAccuracyofCSTsVersusPSG

Sleep Wake Light Deep REMSleepScore 88% 66% 58% 62% 57%FitbitCharge 2

96% 61% 81% 49% 74%

Ōura Ring 96% 48% 65% 51% 61%Beddit N/A 42% 56% 37% N/A

DeZambottietal.ChronobiologyInternational2018;35(4):465-476Tuominen etal.JClinSleepMed.2019;15(3):483–487Zaffaronietal.EngineeringinMedicineandBiology2019,Berlin,Germany,July23-27deZambottietal.Behav SleepMed20181–15.doi:10.1080/15402002.2017.1300587

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BacktotheFuture?

https://www.nuance.com/healthcare/ambient-clinical-intelligence;https://www.engineering.com/DesignerEdge/DesignerEdgeArticles/ArticleID/17664/A-Healthy-Future-for-Artificial-Intelligence-in-Healthcare.aspx

Accessed10/5/2019

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WhatItoldyou

• DefinedAI,machinelearning,andcomputationalphenotyping• AIandsleepstudyscoring• EEGandOSAseverityestimation(diagnosis?)• AIandPAPRxaccuracy• Computationalphenotypingand5-yearsurvivalwithPSG/clinicalvariables• “BrainAge”andAI• Consumersleeptechnologyaccuracycomparison• AIandimplicationsforthefutureofhealthcare


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