discriminating progressive supranuclear palsy from ...progressive supranuclear palsy (psp) (1, 2) is...
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Discriminating Progressive Supranuclear Palsy from Parkinson's Disease usingwearabletechnologyandmachinelearning. Authornamesandaffiliations
Maarten De Vos1, John Prince1, Tim Buchanan PhD2, James J. FitzGerald PhD3,4, and
ChrystalinaA.AntoniadesPhD4*
1 Department of Engineering Science, Institute of Biomedical Engineering, University ofOxford,OldRoadCampusResearchBuilding,OX37DQ,Oxford,UK.2UCBBiopharmaSPRL,Brussels,Belgium
3NuffieldDepartmentofSurgicalSciences,UniversityofOxford,Oxford,OX39DU,UK.
4NuffieldDepartmentofClinicalNeurosciences,NeuroMetrologyLab,UniversityofOxford,
Oxford,OX39DU,UK.
Correspondingauthor
ProfessorChrystalinaA.Antoniades,NuffieldDepartmentofClinicalNeurosciences,Level6,
WestWing,JohnRadcliffeHospital,UniversityofOxford,UnitedKingdom
E-mail:[email protected]
Wordcount:2849(excludingreferences)
References:21
RUNNINGTITLE:WearingtechnologyforPSP
KEYWORDS:ProgressiveSupranuclearPasly,Parkinson’sdisease,gait,wearables,inertial
sensorarray,machinelearning.
FINANCIALDISCLOSURE/CONFLICTOFINTEREST:
MDV,JP,TB,andCAAhavenoconflictsofinterest.
JJFhasperformedconsultancyunrelatedtothesubjectofthisstudyforcompaniesincludingAbbott,Renishaw,andHerantis,andalsohasaresearchgrantfromAbbott,foraprojectunrelatedtothisstudy.
FUNDINGSOURCESFORTHESTUDY:
ThiswasstudywasfundedbyagrantfromtheUCB-OxfordCollaboration.JJFandMDVweresupportedbytheNationalInstituteforHealthResearch(NIHR)OxfordBiomedicalResearchCentre(BRC).
OxQuip is registered on the clinical trials website and this is the CT Identifier:NCT04139551
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AbstractBackground:Progressivesupranuclearpalsy(PSP),aneurodegenerativeconditionsmaybedifficulttodiscriminateclinicallyfromidiopathicParkinson’sdisease(PD).Itiscriticalthatweareabletodothisaccuratelyandasearlyaspossibleinorderthatfuturediseasemodifying therapies forPSPmaybedeployedata stagewhen theyarelikelytohavemaximalbenefit.Analysisofgaitandrelatedtasksisonepossiblemeansofdiscrimination.Research Question: Here we investigate a wearable sensor array coupled withmachinelearningapproachesasameansofdiseaseclassification.Methods:21participantswithPSP,20withPD,and39healthycontrol(HC)subjectsperformed a two minute walk, static sway test, and timed up-and-go task, whilewearing an array of six inertial measurement units. The data were analysed todeterminewhatfeaturesdiscriminatedPSPfromPDandPSPfromHC.Twomachinelearningalgorithmswereapplied,LogisticRegression(LR)andRandomForest(RF).Results:17 features were identified in the combined dataset that containedindependent information. The RF classifier outperformed the LR classifier, andalloweddiscriminationofPSPfromPDwith86%sensitivityand90%specificity,andPSP fromHCwith 90% sensitivity and 97% specificity. Using data from the singlelumbar sensor only resulted in only amodest reduction in classification accuracy,whichcouldberestoredusing3sensors(lumbar,rightarmandfoot).Howeverformaximumspecificitythefullsixsensorarraywasneeded.Significance:Awearable sensor array coupledwithmachine learningmethods canaccurately discriminate PSP from PD. Choice of array complexity depends oncontext; for diagnostic purposes a high specificity is needed suggesting the morecomplete array is advantageous, while for subsequent disease tracking a simplersystemmaysuffice.
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Introduction
Progressivesupranuclearpalsy(PSP)(1,2) isanatypicalparkinsoniandisorderthat
can sometimes be difficult to discriminate clinically from the much commoner
Parkinson’s disease (3). It is characterized by vertical supranuclear gaze palsy,
postural instability andaxial rigidityaswell asmild cognitive impairment (1) (4-6).
Recently,theMovementDisordersSociety(MDS)studygrouphasproposedasetof
diagnosticcriteriaforPSP,withtheaimofimprovingthedetectionofthediseasein
clinicalpracticeandresearch(7).
Comparative studies using accelerometers (8) have shown many shared gait
abnormalities(9)(10-12)includingdecreasesinvelocity,steplength,cadence,and
meanacceleration.Somedifferenceshavealsobeenfound,includinglowervertical
displacementandhigheraccelerationinPSP.Inthispaperweexaminetheabilityof
a wearable array of inertial measurement units (IMUs), coupled with machine
learningalgorithms, todistinguishPSPpatients fromPDpatientsand fromhealthy
controls.
Therearea largeandgrowingnumberofwearable technologies (13,14)available
for characterizing and measuring the motor features of neurodegenerative
diseases(15-17).
When deciding whatmeasurement technology to use there are twomajor issues
thatmust be considered. One is the setting for themeasurement, which can be
broadlydividedintolaboratorymeasurementsorambulatorymeasurementsoutside
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the laboratory (e.g. at home). The former will permit closely controlled and
supervisedconditionsgivingthebestchanceofobtainingahighqualitystandardized
dataset, and is well suited to tasks like detailed measurement of individual
components of the gait cycle. However there is growing interest in longer term
ambulatorymeasurementbecausesnapshot laboratoryrecordingsmaynotgivean
accurateoverallpicture,duetomanyfactorssuchasvariabilityofsymptomsbytime
ofday,inconsistenttimingofmedication,orthestressofbeingtestedinahospital
environment.Laboratorymeasurementsarealsounlikelytocaptureinfrequentbut
important features such as falls or near-falls, and a ‘realworld’ environmentmay
possiblybetterelicitfeaturesthatareofdirectclinicalrelevancetothepatient.
Thesecond issue is thechoiceofdevice. Inparticular, there isaconflictbetween
maximisingdataandminimisingtheeffortinvolvedinobtainingit.Atoneendofthe
spectrum, it is possible to have body worn arrays comprising numerous inertial
measurement units attached to all four limbs and the trunk, streaming tens of
megabytesperminute. At theoppositeextreme, some investigators are trying to
develop systems based on single sensors, even those built into consumer
smartphones,sothatallthatisrequiredisanapprunninginthebackground.There
is no doubt that the complex systems will generate much more complete
information;thequestioniscanthesimplersystemsanswerthesamediagnosticand
measurement questions acceptably well, despite having a comparatively meagre
dataset?
Thetwoissuesareclearlylinked.Applyingacomplexsysteminalaboratorysetting
is straightforward and is logical given the intent of obtaining detailed data in a
concentratedperiodof time. Usinga complex systemathome isanothermatter:
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donninganddoffingmaynotbeeasy,andthemultiplesensorsmaygetinthewayof
everydayactivities.Thereisapremiumonusingassimpleasystemasiscompatible
withobtainingsufficientdatatobeuseful.
Our aim in this study is to answer two questions. Firstly, we will use a complex
laboratory systemwith sensorsoneach limband the trunk, to recorddataduring
commonly applied gait-related tasks. We will explore how well the data can
distinguishPSPpatientsfromPDpatientsandfromhealthycontrol(HC)participants,
and what the most essential distinguishing features within the dataset are.
Secondly,wewillanalysewhichsensors/bodylocationsarenecessarytoacquirethis
data.Theaimistodeterminehowfarthesensorarraycanbesimplifiedwhilestill
yieldingsatisfactoryresults.Itishopedthatthiswillhelpmaximisecompliancewith
future study protocols while avoiding collecting an inadequate dataset due to
oversimplification.
Materials&Methods
Participants
The participants in this study were recruited as part of the Oxford study of
Quantification in Parkinsonism (OxQUIP); a large clinical observational study being
conductedattheJohnRadcliffeHospital,Oxford.Werecruited20participantswith
PD,21participantswithPSP,and39healthycontrolparticipants.Allhealthycontrol
participantswere spousesof thePDor PSPparticipants. Thedemographics of the
participantsaresummarisedinTable1.
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Sensorarrayandsoftware
Participantsworeasystemconsistingofsixsynchronizedinertialmeasurementunits
(IMUs) (OpalTM,APDM,Portland,USA)thatwirelessly transmitteddatatosoftware
(MobilityLabTM,APDM)runningonanearbylaptop.TheIMUswerepositionedover
the lumbarspine,sternum, leftandrightwrists,and leftandrightfeet.Allsensors
provided tri-axial accelerometer, tri-axial gyroscope, and tri-axial magnetometer
signals at a frequency of 100Hz. Participants performed three tasks: two minute
walk,swaytest,andtimedup-and-go(TUG).Usingthewaveformsfromallsensors,
theMobilityLabsoftwareautomaticallyextractsarangeofclinicalfeaturesspecific
to the three tasks.A full listof the task featuresareprovided in figure2. The full
featuresetincluded109parametersfromanalysisofthegaittask,33fromthesway
test,and14fromtheTUG.
Anyparticipantstakingmedicationwererecordedinthe‘ONmedication’state.
Tasks
Allparticipantscompletedatwominutewalkonthesamestraightandlevelsurface
torecordtheirgait.Next,tomeasuresway,participantswereaskedtostandupright
andasstillaspossibleforthirtysecondswiththeireyesclosedonafirmsurface.A
woodentemplatewasplacedonthefloorsoastoensureallparticipants’feetwere
thesamedistanceapartforthetest.TheTUGwasthenrepeatedthreetimes.Each
repetition entailed the participant starting in a sitting position for three seconds,
followedby standingupwhen instructed,walking forward3metres, performinga
180degreeturn,walkingbacktothechairandsittingbackdown.
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HypothesisTesting
The first aspect of this analysis aims to identify features that demonstrate
statisticallysignificantdifferencesbetweenthediseasegroups.Specifically,weaim
todeterminewhetherthePSPgroupshowsadifferencetoHCandPDparticipantsin
a feature.Eachparticipantcontributesone instance ineachof thethreetasks.For
each task type (gait, sway, and TUG), independent t-tests are used to inspect for
statistical significance between the three disease groups for all features using a
significancelevelof0.05.
DiseaseClassification
Inorder toassess towhatextentPSP canbeautomaticallydiscriminated fromPD
and HC subjects based on the measured features, we performed automated
classification with a machine-learning algorithm. For each test, repeated 10-fold
crossvalidationisperformedbasedonthefullfeaturesetfromeachtestseparately.
Thismeansthatthefulldatasetissplitinrandomlyinto10subsets,where9subsets
areusedfortrainingthemachinelearningmodelandtheremainingsubsetisusedas
an independent validation set. This training and validation is repeated 10 times
whereeachtimeadifferentindependentvalidationsetisused.Withineachrepeat
of cross validation, the cross sectional baseline subset undergoes minority class
balancing prior to being assigned to a fold. Within each fold, the training and
validation setsundergo zero-meanunit-variancenormalisationwith respect to the
meanandstandarddeviationofthetrainingset.Duetothelikelihoodthatmanyof
the features within each test are correlated (e.g. Gait Speed Left and Gait Speed
Right),featureselection(LeastAbsoluteShrinkageandSelectionOperator,LASSO)is
performed on each training set, with the selected features subsequently being
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extracted fromthevalidationset.Twoclassifierswereused. ALogisticRegression
(LR) classifierwasuseddue to its popularity in the clinical field.ARandomForest
(RF)classifierwasalsoused,withoutfeaturereductionasRandomForestclassifiers
areknowntodealwellwithahighdimensionalfeatureset.Wereporttheaccuracy,
sensitivity,andspecificityforeachtaskseparatelyandalltaskscombined.
Reducedsensorset
In order to assess the necessity or not of using a large sensor set, we took the
featuresselectedbyLASSOforall taskscombinedandassessedfromwhichsensor
thosefeaturesarecomputed.Wethenrepeatedtheclassificationprocedureusing
only those features extracted from the lumbar sensor, because this sensor alone
accountedforamajorityofthefeaturesidentifiedbyLASSO.Wefurtherrepeated
the classificationusingdataobtainable from the lumbar sensorplus the right arm
andrightlegsensors,andreportedthesameperformancemetrics.
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Results
Participants
Thedemographics,clinicalcharacteristics,andratingscalescoresoftheparticipants
foreachgroupareshownintable1.
Figure1 shows thedistributionsof someof the feature valuesextracted from the
three tasks thatwere found todiffer significantlybetweenPSPand the twoother
groups.Theboxplots showthe rangeofvalues foreachof these features ineach
participant group, with individual data points overlaid in grey. Note that there is
considerable redundancy amongst the large number of features, and not all
measurements that were found to be significant individually emerged from the
LASSOasindependentpredictors.Forexampleintheswaytest,meancoronalsway
velocitywasindividuallysignificant,butthisdidnotfeatureintheLASSOresult.
Table2showsthediseaseclassificationaccuracyunderanumberofconditions.The
upper three rows of the table show the accuracy, sensitivity, and specificity of
discriminationbetweenPSPandPDandbetweenPSPandHC,foreachofthethree
tasks separately as indicated in the left column. It can be seen that of the three
tasks,swayismuchlesseffectivethantheothertwoatdiscriminatingbetweenPSP
andPD.Thefourthrowoftable2givestheresultsofanalysingthecombinedfeature
setfromallthreetasks;thisperformsbetterthananyofthe3tasksindividually.
ItisclearthattheRFclassifierperformsbetterthantheLRclassifieroverall.When
distinguishingPSPfromPDthesensitivity,specificity,andaccuracyofRFwasasgood
asorbetterthanLRineveryconditioninthetable.Thegreatestdifferencewas15
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percentage points in specificity using the combined tasks and full sensor set (75%
withLRversus90%withRF).
Table 3 lists the features that emerged from the LASSO analysis of the combined
tasks as the most important discriminators between the groups, with their
weightingsandwhichsensor inthearraythedataareobtainedfromineachcase.
Notably, the lumbar sensorprovides thedata for tenof the seventeen features in
thislist,comparedtojustonefeaturethatisdependentonthesternalsensor.This
promptsonetoaskwhattheresultswouldbeifthelumbarsensorwereusedalone.
The answer (table 2 row 5) is that using only one sensor in the lumbar position
reducestheaccuracyofbothclassifiersmodestly,from80%to78%withLRandfrom
88%to85%withRF.
We thenexplored theeffectof addingonearmandone leg sensor to the lumbar
sensor, making a three sensor array including lumbar, right arm and right foot.
Where LASSO had identified left sided parameter we substituted the equivalent
parameter from the opposite side, making the assumption that, for example,
cadence measured from either leg would report a similar value. Using this
intermediatesizenetwork,theaccuracywithbothclassifierswasthesameasforthe
fullsixsensornetwork(table2row6).Notablyhowever,thehighspecificity(90%)
observedwith the 6 sensor networkwas reduced to 85%with the lumbar sensor
alone,andthiswasnotrecoveredusing3sensors.
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Discussion
Wehaveshownthatitispossiblewithabody-wornIMUarrayandmachinelearning
methodstodifferentiatePSPfromPDandPSPfromcontrol,withahighdegreeof
accuracy. In order to ensure the robustness of these findings we used separate
trainingandvalidationdatasets fortheanalysis,andtoprovidevalidationweused
two different classification algorithms. Overall, the Random Forest classifier
performed better than the Logistic Regression classifier. LR is probably the most
commonly used classifier in biomedical research and therefore the apparent
superiorityofRFisanimportantfinding.
Arecentreviewfound78publishedstudiesofIMUbasedgaitanalysis,butonly16%
oftheseusedmorethanonesensor(18).Wefoundjusttwopreviousstudieslooking
atgaitinPSP.OneoftheseusedasingleIMUinthelumbarregion(8),anddetected
only a difference in vertical displacement during gait between PSP and PD. The
other used two sensors, one attached to each foot (19), and identified significant
differencesingaitspeedandcadence.Inbothcasestheinformationobtainedisfar
lessdetailedthanthatobtainedfrommultiplesensorsinthepresentstudy.
Asexpectedthere isahighdegreeof redundancy in themeasuredfeatureswithin
andacrossthethreetasksused.Thisiswellillustratedbythefactthat,forexample,
the mean velocity of postural sway in the coronal plane was significantly and
substantially differentbetweenPSP and theother groups, yet didnotoccur as an
independentpredictorinthefeaturesetselectedbytheLASSOanalysis.Indeedonly
oneswayvariablewasfoundtocontainindependentinformation.
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From the point of view of overall classification accuracy, it may be possible to
simplifythesensorarray.Ofthe17keyparametersidentifiedbytheLASSOanalysis,
10couldbeobtained from just the lumbar sensor. IndistinguishingPSP fromHC,
theaccuracy,sensitivity,andspecificityusingjustthissensorwasvirtually identical
tothatobtainedusingthefullsensorarray.Usingjustthelumbarsensordiddegrade
performance in termsof test accuracywhendifferentiatingPSP fromPD,butonly
modestly (for the RF classifier there was a test accuracy of 88% with six sensors
versus 85% with the lumbar sensor only). Using an intermediate 3 sensor array
(lumbar, right arm, right foot) gave a test accuracy as good as the full six sensor
array.
In some respects however themost important parameter is specificity, because a
higher specificity increases positive predictive value (PPV), which is critical if PSP
specific treatments are tobe initiatedbasedon the result. The specificityof 90%
usingthe6sensorarrayandtheRFclassifierisremarkablygood.Previousbiomarker
studiesusingdiverseapproacheshavenotyieldedspecificitiesashighas this. For
example,MRImorphometryofthebrainstemgaveaspecificityof85%(20),whilea
meta-analysis of studies of probably the most promising CSF biomarker,
neurofilament light chain, gave a specificity of 81%(21). Because of the low
prevalenceofPSPamongstpatientspresentingwithparkinsonism,eventhehigher
85%figuretranslatedintoaPPVofjust57%(20).TheimprovementinPPVbetween
atestwith85%and90%specificityissubstantial.Inmovingfromthefull6sensors
to just the lumbar sensor, the 90% specificity of our RF classifier fell to 85%, an
important reduction in efficacy. Performance was not restored by adding in two
moresensors.
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Itshouldbenotedthatthejobthattheclassifierswereaskedtodointhisstudyis
considerably easier than the applications to which we hope the approach will
ultimatelyextend. It isnosurprisetoseeclassificationaccuraciesinexcessof90%
when discriminating PSP patients from controls. The fact that the classification
accuracy between PSP and PD was not far behind (88% with RF) may seem
impressive,butwebeganwithgroupsofclear-cutPSPandPD,whowouldbereadily
distinguishableclinicallybyanexperiencedmovementdisordersneurologist.Forthe
technique to be most useful, it must do something that such a person finds
challenging. The performance differential between more complete and simpler
sensor sets may well widen with more difficult questions and will need to be
carefullyevaluatedineachsituation.
The most obvious next step in testing this approach would be to evaluate its
effectiveness in the differential diagnosis of these conditions at an earlier stage,
when there is still substantial clinical diagnostic uncertainty. Thiswill require the
captureofalargeincidentcohortofparkinsonianpatients,inorderthatitturnsout
eventuallytocontainsufficientnumbersofcasesthatmanifestasPSP,ratherthan
thefarcommonerPD.Theothertaskthatcouldbenefitfromthemachinelearning
approach is the related one of disease severity quantification and progression
tracking. This is an area of great interest because objective numerical biomarkers
capableofdoingthisinPSP(andPD)arelacking,yetverymuchneededfortrialsof
potentialdiseasemodifyingdrugs.
In conclusion, the answer to the question of whether the sensor network can be
simplifieddependsoncontextandtheexactparametersweareinterestedin.Itmay
be that themore complex sensor array is better suited to the diagnostic setting,
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where specificity is paramount, but then a simpler arrangement can be used for
quantificationandtrackingoncethediagnosisissecure.
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ACKNOWLEDGMENTS
WethankthepatientsandtheirfamiliesfortheirendlesssupportduringtheOxQUIPstudy.
AUTHORS’ROLES
Researchproject:Conception,organization,executionCAA,JFF
Statisticalanalysis:Design,execution,review&critiqueMDV,JP,JJFandCAA
Manuscript:Drafting,review&critiqueMDV,JP,TB,JJFandCAA
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Figure1:ExamplesofgaitparametersthatdistinguishPSPfromPDandHC.A:Gaitcadence;B:Meanposturalswayvelocityinthecoronalplaneduringtheswaytest;C:Meantimetakentositfromstandingduringthetimedup-and-go(TUG)task;D:Meantimetakentoturnduringthegaittask;E:MeantimetakentoturnduringtheTUGtask;F:Standarddeviationoftimetakentoturnduringthegaittask.Notethatthe large number of parameters generated by the three tasks have considerableredundancy so that some parameters that are significantly different between theconditionsindividuallyarenotintheLASSOoutput.
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List of features that are quantified for each task Gait: 'AnticipatoryPosturalAdjustment-APADuration(s)''AnticipatoryPosturalAdjustment-FirstStepDuration(s)''AnticipatoryPosturalAdjustment-FirstStepRangeofMotion(degrees)''AnticipatoryPosturalAdjustment-ForwardAPAPeak(m/s^2)''AnticipatoryPosturalAdjustment-LateralAPAPeak(m/s^2)''Duration(s)''Gait-LowerLimb-CadenceL(steps/min)''Gait-LowerLimb-CadenceR(steps/min)''Gait-LowerLimb-DoubleSupportL(%GCT)''Gait-LowerLimb-DoubleSupportR(%GCT)''Gait-LowerLimb-ElevationatMidswingL(cm)''Gait-LowerLimb-ElevationatMidswingR(cm)''Gait-LowerLimb-GaitCycleDurationL(s)''Gait-LowerLimb-GaitCycleDurationR(s)''Gait-LowerLimb-GaitSpeedL(m/s)''Gait-LowerLimb-GaitSpeedR(m/s)''Gait-LowerLimb-LateralStepVariabilityL(cm)''Gait-LowerLimb-LateralStepVariabilityR(cm)''Gait-LowerLimb-CircumductionL(cm)''Gait-LowerLimb-CircumductionR(cm)''Gait-LowerLimb-N(#)''Gait-LowerLimb-FootStrikeAngleL(degrees)''Gait-LowerLimb-FootStrikeAngleR(degrees)''Gait-LowerLimb-ToeOffAngleL(degrees)''Gait-LowerLimb-ToeOffAngleR(degrees)''Gait-LowerLimb-SingleLimbSupportL(%GCT)''Gait-LowerLimb-SingleLimbSupportR(%GCT)''Gait-LowerLimb-StanceL(%GCT)''Gait-LowerLimb-StanceR(%GCT)''Gait-LowerLimb-StepDurationL(s)''Gait-LowerLimb-StepDurationR(s)''Gait-LowerLimb-StrideLengthL(m)''Gait-LowerLimb-StrideLengthR(m)''Gait-LowerLimb-SwingL(%GCT)''Gait-LowerLimb-SwingR(%GCT)''Gait-LowerLimb-TerminalDoubleSupportL(%GCT)''Gait-LowerLimb-TerminalDoubleSupportR(%GCT)''Gait-LowerLimb-ToeOutAngleL(degrees)''Gait-LowerLimb-ToeOutAngleR(degrees)''Gait-Lumbar-CoronalRangeofMotion(degrees)''Gait-Lumbar-SagittalRangeofMotion(degrees)''Gait-Lumbar-TransverseRangeofMotion(degrees)''Gait-Trunk-CoronalRangeofMotion(degrees)'
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'Gait-Trunk-SagittalRangeofMotion(degrees)''Gait-Trunk-TransverseRangeofMotion(degrees)''Gait-UpperLimb-ArmSwingVelocityL(degrees/s)''Gait-UpperLimb-ArmSwingVelocityR(degrees/s)''Gait-UpperLimb-ArmRangeofMotionL(degrees)''Gait-UpperLimb-ArmRangeofMotionR(degrees)''Turns-Angle(degrees)''Turns-Duration(s)''Turns-N(#)''Turns-TurnVelocity(degrees/s)''Turns-StepsinTurn(#)''AnticipatoryPosturalAdjustment-APADuration(s)STD''AnticipatoryPosturalAdjustment-FirstStepDuration(s)STD''AnticipatoryPosturalAdjustment-FirstStepRangeofMotion(degrees)STD''AnticipatoryPosturalAdjustment-ForwardAPAPeak(m/s^2)STD''AnticipatoryPosturalAdjustment-LateralAPAPeak(m/s^2)STD''Duration(s)STD''Gait-LowerLimb-CadenceL(steps/min)STD''Gait-LowerLimb-CadenceR(steps/min)STD''Gait-LowerLimb-DoubleSupportL(%GCT)STD''Gait-LowerLimb-DoubleSupportR(%GCT)STD''Gait-LowerLimb-ElevationatMidswingL(cm)STD''Gait-LowerLimb-ElevationatMidswingR(cm)STD''Gait-LowerLimb-GaitCycleDurationL(s)STD''Gait-LowerLimb-GaitCycleDurationR(s)STD''Gait-LowerLimb-GaitSpeedL(m/s)STD''Gait-LowerLimb-GaitSpeedR(m/s)STD''Gait-LowerLimb-LateralStepVariabilityL(cm)STD''Gait-LowerLimb-LateralStepVariabilityR(cm)STD''Gait-LowerLimb-CircumductionL(cm)STD''Gait-LowerLimb-CircumductionR(cm)STD''Gait-LowerLimb-N(#)STD''Gait-LowerLimb-FootStrikeAngleL(degrees)STD''Gait-LowerLimb-FootStrikeAngleR(degrees)STD''Gait-LowerLimb-ToeOffAngleL(degrees)STD''Gait-LowerLimb-ToeOffAngleR(degrees)STD''Gait-LowerLimb-SingleLimbSupportL(%GCT)STD''Gait-LowerLimb-SingleLimbSupportR(%GCT)STD''Gait-LowerLimb-StanceL(%GCT)STD''Gait-LowerLimb-StanceR(%GCT)STD''Gait-LowerLimb-StepDurationL(s)STD''Gait-LowerLimb-StepDurationR(s)STD''Gait-LowerLimb-StrideLengthL(m)STD''Gait-LowerLimb-StrideLengthR(m)STD''Gait-LowerLimb-SwingL(%GCT)STD''Gait-LowerLimb-SwingR(%GCT)STD'
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'Gait-LowerLimb-TerminalDoubleSupportL(%GCT)STD''Gait-LowerLimb-TerminalDoubleSupportR(%GCT)STD''Gait-LowerLimb-ToeOutAngleL(degrees)STD''Gait-LowerLimb-ToeOutAngleR(degrees)STD''Gait-Lumbar-CoronalRangeofMotion(degrees)STD''Gait-Lumbar-SagittalRangeofMotion(degrees)STD''Gait-Lumbar-TransverseRangeofMotion(degrees)STD''Gait-Trunk-CoronalRangeofMotion(degrees)STD''Gait-Trunk-SagittalRangeofMotion(degrees)STD''Gait-Trunk-TransverseRangeofMotion(degrees)STD''Gait-UpperLimb-ArmSwingVelocityL(degrees/s)STD''Gait-UpperLimb-ArmSwingVelocityR(degrees/s)STD''Gait-UpperLimb-ArmRangeofMotionL(degrees)STD''Gait-UpperLimb-ArmRangeofMotionR(degrees)STD''Turns-Angle(degrees)STD''Turns-Duration(s)STD''Turns-N(#)STD''Turns-TurnVelocity(degrees/s)STD''Turns-StepsinTurn(#)STD'
Sway: 'PosturalSway-Acc-95%EllipseAxis1Radius(m/s^2)''PosturalSway-Acc-95%EllipseAxis2Radius(m/s^2)''PosturalSway-Acc-95%EllipseRotation(m/s^2)''PosturalSway-Acc-95%EllipseSwayArea(m^2/s^4)''PosturalSway-Acc-CentroidalFrequency(Hz)''PosturalSway-Acc-CentroidalFrequency(Coronal)(Hz)''PosturalSway-Acc-CentroidalFrequency(Sagittal)(Hz)''PosturalSway-Acc-FrequencyDispersion(AD)''PosturalSway-Acc-FrequencyDispersion(Coronal)(AD)''PosturalSway-Acc-FrequencyDispersion(Sagittal)(AD)''PosturalSway-Acc-Jerk(m^2/s^5)''PosturalSway-Acc-Jerk(Coronal)(m^2/s^5)''PosturalSway-Acc-Jerk(Sagittal)(m^2/s^5)''PosturalSway-Acc-MeanVelocity(m/s)''PosturalSway-Acc-MeanVelocity(Coronal)(m/s)''PosturalSway-Acc-MeanVelocity(Sagittal)(m/s)''PosturalSway-Acc-PathLength(m/s^2)''PosturalSway-Acc-PathLength(Coronal)(m/s^2)''PosturalSway-Acc-PathLength(Sagittal)(m/s^2)''PosturalSway-Acc-RMSSway(m/s^2)''PosturalSway-Acc-RMSSway(Coronal)(m/s^2)''PosturalSway-Acc-RMSSway(Sagittal)(m/s^2)''PosturalSway-Acc-Range(m/s^2)''PosturalSway-Acc-Range(Coronal)(m/s^2)''PosturalSway-Acc-Range(Sagittal)(m/s^2)''PosturalSway-Angles-SwayAreaRadius(Coronal)(degrees)'
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'PosturalSway-Angles-95%EllipseAxis2Radius(degrees)''PosturalSway-Angles-SwayAreaRotation(degrees)''PosturalSway-Angles-SwayArea(degrees^2)''PosturalSway-Angles-Duration(s)''PosturalSway-Angles-RMSSway(degrees)''PosturalSway-Angles-RMSSway(Coronal)(degrees)''PosturalSway-Angles-RMSSway(Sagittal)(degrees)'
TUG: 'Duration(s)''SittoStand-Duration(s)''SittoStand-LeanAngle(degrees)''SittoStand-N(#)''StandtoSit-Duration(s)''StandtoSit-LeanAngle(degrees)''StandtoSit-N(#)''Turns-Angle(degrees)''Turns-Duration(s)''Turns-N(#)''Turns-TurnVelocity(degrees/s)''Turns-Angle(degrees)STD''Turns-Duration(s)STD''Turns-TurnVelocity(degrees/s)STD'
Figure2.Afulllistoftaskfeatures.
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References
1. LitvanI,AgidY,CalneD,CampbellG,DuboisB,DuvoisinRC,etal.Clinicalresearchcriteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewskisyndrome):reportoftheNINDS-SPSPinternationalworkshop.Neurology.1996Jul;47(1):1-9.PubMedPMID:8710059.eng.2. Kuniyoshi S, Riley DE, Zee DS, Reich SG, Whitney C, Leigh RJ. Distinguishingprogressivesupranuclearpalsy fromother formsofParkinson'sdisease:evaluationofnewsigns. Annals of the New York Academy of Sciences. 2002 Apr;956:484-6. PubMed PMID:11960847.eng.3. Fearnley JM, Lees AJ. Ageing and Parkinson's disease: substantia nigra regionalselectivity.Brain.1991Oct;114(Pt5):2283-301.PubMedPMID:1933245.eng.4. BoxerAL,Yu JT,GolbeLI, Litvan I, LangAE,HoglingerGU.Advances inprogressivesupranuclearpalsy:newdiagnosticcriteria,biomarkers,andtherapeuticapproaches.Lancetneurology. 2017 Jul;16(7):552-63. PubMed PMID: 28653647. Pubmed Central PMCID:5802400.5. Lamb R, Rohrer JD, Lees AJ, Morris HR. Progressive Supranuclear Palsy andCorticobasal Degeneration: Pathophysiology and Treatment Options. Current treatmentoptionsinneurology.2016Sep;18(9):42.PubMedPMID:27526039.PubmedCentralPMCID:4985534.6. Schrag A, Ben-Shlomo Y, Quinn NP. Prevalence of progressive supranuclear palsyandmultiplesystematrophy:across-sectionalstudy.Lancet.1999Nov20;354(9192):1771-5.PubMedPMID:10577638.eng.7. HoglingerGU,RespondekG,StamelouM,KurzC,JosephsKA,LangAE,etal.Clinicaldiagnosis of progressive supranuclear palsy: Themovement disorder society criteria.MovDisord.2017Jun;32(6):853-64.PubMedPMID:28467028.PubmedCentralPMCID:5516529.8. Hatanaka N, Sato K, Hishikawa N, Takemoto M, Ohta Y, Yamashita T, et al.Comparative Gait Analysis in Progressive Supranuclear Palsy and Parkinson's Disease.Europeanneurology.2016;75(5-6):282-9.PubMedPMID:27288001.9. Murray MP, Sepic SB, Gardner GM, Downs WJ. Walking patterns of men withparkinsonism. American journal of physical medicine. 1978 Dec;57(6):278-94. PubMedPMID:742658.10. Morris ME, Martin CL, Schenkman ML. Striding out with Parkinson disease:evidence-basedphysicaltherapyforgaitdisorders.Physicaltherapy.2010Feb;90(2):280-8.PubMedPMID:20022998.PubmedCentralPMCID:2816030.11. Hausdorff JM,CudkowiczME,FirtionR,Wei JY,GoldbergerAL.Gaitvariabilityandbasalgangliadisorders:stride-to-stridevariationsofgaitcycletiminginParkinson'sdiseaseandHuntington'sdisease.MovDisord.1998May;13(3):428-37.PubMedPMID:9613733.12. Egerton T,Williams DR, Iansek R. Comparison of gait in progressive supranuclearpalsy, Parkinson's disease and healthy older adults. BMC neurology. 2012 Oct 2;12:116.PubMedPMID:23031506.PubmedCentralPMCID:3517411.13. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, et al. Asystematic review of the characteristics and validity of monitoring technologies to assessParkinson's disease. Journal of neuroengineering and rehabilitation. 2016 Mar 12;13:24.PubMedPMID:26969628.PubmedCentralPMCID:4788909.14. BrognaraL,PalumboP,GrimmB,PalmeriniL.AssessingGaitinParkinson'sDiseaseUsingWearableMotionSensors:ASystematicReview.Diseases.2019Feb5;7(1).PubMedPMID:30764502.PubmedCentralPMCID:6473911.
All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint (whichthis version posted November 6, 2019. .https://doi.org/10.1101/19006866doi: medRxiv preprint
23
15. BastonC,ManciniM,SchoneburgB,HorakF,RocchiL.Posturalstrategiesassessedwithinertialsensorsinhealthyandparkinsoniansubjects.Gait&posture.2014;40(1):70-5.PubMedPMID:24656713.PubmedCentralPMCID:4383136.16. ManciniM,Carlson-KuhtaP,ZampieriC,NuttJG,ChiariL,HorakFB.Posturalswayasamarker of progression in Parkinson's disease: a pilot longitudinal study. Gait& posture.2012Jul;36(3):471-6.PubMedPMID:22750016.PubmedCentralPMCID:3894847.17. ManciniM, Zampieri C, Carlson-Kuhta P, Chiari L, Horak FB. Anticipatory posturaladjustments prior to step initiation are hypometric in untreated Parkinson's disease: anaccelerometer-based approach. Eur J Neurol. 2009 Sep;16(9):1028-34. PubMed PMID:19473350.PubmedCentralPMCID:2840629.18. Vienne A, Barrois RP, Buffat S, Ricard D, Vidal PP. Inertial Sensors to Assess GaitQuality in Patients with Neurological Disorders: A Systematic Review of Technical andAnalyticalChallenges.FrontPsychol.2017;8:817.PubMedPMID:28572784.PubmedCentralPMCID:PMC5435996.19. Raccagni C, Gassner H, Eschlboeck S, Boesch S, Krismer F, Seppi K, et al. Sensor-based gait analysis in atypical parkinsonian disorders. Brain Behav. 2018 Jun;8(6):e00977.PubMedPMID:29733529.PubmedCentralPMCID:PMC5991583.20. Kim YH, Ma HI, Kim YJ. Utility of the Midbrain Tegmentum Diameter in theDifferentialDiagnosisofProgressiveSupranuclearPalsyfromIdiopathicParkinson'sDisease.Journal of clinical neurology. 2015 Jul;11(3):268-74. PubMed PMID: 26174787. PubmedCentralPMCID:4507382.21. Ge F, Ding J, Liu Y, Lin H, Chang T. Cerebrospinal fluid NFL in the differentialdiagnosis of parkinsonian disorders: A meta-analysis. Neuroscience letters. 2018 Oct15;685:35-41.PubMedPMID:30036569.
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PD(n=20)
Mean(range)
PSP(n=21)
Mean(range)
HC(n=39)
Mean(range)
Age,yrs 66.4(50-79) 71(63-89) 67.1(51-82)
Gender,Male/Female 11:9 12:9 19:20
Diseaseduration(yrs) 11.4 2.0 NA
UPDRSmotorscore(PartIII) 27.9(9-52) 44.6(21-72) 3.1(0-12)
MMSE 26.6(25-29) 25.8(20-30) 27.6(26-30)
MOCA 26.6(24-30) 22.0(12-29) 28.5(27-30)
Table 1. Demographics, clinical characteristics and cognitive scores in the 3 groups. PD =
Parkinson’sdisease,HC=healthycontrolsandPSP=ProgressiveSupranuclearPalsy.UPDRS=
UnifiedParkinson’sDiseaseRatingScale,MMSE=MiniMentalstateexamination.
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Task Sensorsused ComparisonLogisticRegression RandomForest
Acc(%) Sens(%) Spec(%) Acc(%)Sens(%) Spec(%)
GaitLumbar,sternal,both
arms,bothfeetPSPvsHC 87 86 87 91 85 94
PSPvsPD 80 81 75 83 81 85
Sway Lumbar,sternal,botharms,bothfeet
PSPvsHC 68 52 77 82 67 90
PSPvsPD 63 66 60 63 67 60
TUGLumbar,sternal,both
arms,bothfeetPSPvsHC 90 81 97 92 86 95
PSPvsPD 70 71 70 83 86 80
Combined Lumbar,sternal,botharms,bothfeet
PSPvsHC 93 86 97 95 90 97
PSPvsPD 80 85 75 88 86 90
Combined LumbaronlyPSPvsHC 93 85 97 95 90 97
PSPvsPD 78 76 80 85 86 85
Combined Lumbar,rightarm,rightfoot
PSPvsHC 93 89 96 93 90 97
PSPvsPD 80 85 75 88 90 85
Table 2. Classification results when automatically classifying different patients based on their gait,
sway,andTUGsignaturesusingtheentiresix-sensorarray(firstthreerowsoftable).Swayistheleast
informative test. Classification accuracy is improvedwhen the feature sets fromall three tasks are
merged (fourth row). TheRandomForest (RF) classifier performsbetter than the logistic regression
(LR)classifier.Usingdatafromthelumbarsensoralone(row5)reducesaccuracymodestly,butthiscan
berecoveredbyaddingbackinarmandfootsensorsfromonesideofthebody(lastrowoftable).The
high specificityofPSPversusPDclassificationobtainedwithRF (90%) isonly seenwhenusing the6
sensorarray.
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Activity Feature Weighting SensorAll
sensorsLumbaronly
Lumbar,arm,leg
TUG 'Turns-Duration(s)' 0.11328 Lumbar X X X
XX 'Gait-LowerLimb-CadenceL(steps/min)STD' 0.09398 LeftLeg X
X
TUG 'Turns-Angle(degrees)STD' 0.06248 Lumbar X X X
TUG 'SittoStand-Duration(s)' 0.04518 Lumbar X X X
Gait 'Gait-Trunk-SagittalRangeofMotion(degrees)STD' -0.03971 Trunk X
Gait 'Gait-LowerLimb-ToeOffAngleR(degrees)STD' 0.03182 RightLeg X
X
TUG 'Turns-Duration(s)STD' 0.03071 Lumbar X X X
TUG 'StandtoSit-LeanAngle(degrees)' -0.03009 Lumbar X X X
TUG 'Turns-Angle(degrees)' -0.02602 Lumbar X X X
TUG 'Duration(s)' 0.01957 Lumbar X X X
Gait 'Gait-UpperLimb-ArmSwingVelocityR(degrees/s)' -0.01876 RightArm X
X
Gait 'Gait-UpperLimb-ArmRangeofMotionR(degrees)' -0.01631 RightArm X
X
Gait 'Turns-StepsinTurn(#)STD' 0.01618 Lumbar X X X
Sway 'PosturalSway-Acc-FrequencyDispersion(AD)' 0.01395 Lumbar X X X
Gait 'Gait-UpperLimb-ArmRangeofMotionL(degrees)' -0.00735 LeftArm X
X
Gait 'Gait-LowerLimb-ToeOutAngleL(degrees)' 0.00101 LeftLeg X
X
Gait 'Turns-Duration(s)' 0.00025 Lumbar X X X
Table 3. Key parameters emerging from LASSO analysis discriminating PSP from PD and HC subjects,
togetherwiththeirrelativeimportance(asreflectedbytheweights).The‘sensor’columnexplainswhich
ofthesensorsinthearrayisresponsibleforcollectingthedatadescribed,andtheactivitycolumnonthe
leftshowsduringwhichtest the featurehasbeencollected.Thethreecolumnsontherightshowhow
muchofthefeaturesetisavailabledependingontheextentofsensorarrayused.
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