Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: minor_revision

Abstract
Diagnosingsleepdisorderedbreathingrequiresmanualannotationofevents
fromsleepstudies,suchasnocturnalpolysomnography,aprocessthatistime-
intensive,costly,andpronetointer-ratervariability.Automaticapproaches
existbutlackgeneralizabilityduetosignalvariabilityacrosscenters.We
developanautomaticapneicbreathingeventdetectortolocalizeandclassify
obstructiveapneas,centralapneas,hypopneas,andisolatedrespiratoryevents
withoutarousalsordesaturations.Themodelistrainedon5456poly-
somnographiesandtestedon1099polysomnographiesfromsixcohortsuses
anend-to-enddeeplearningarchitecture.Themodel’spredictionsshowa
strongcorrelationwithexpertannotationsforapnea-hypopneaindex(r²=
0.84)andachieveanF1scoreof0.78acrossapneaeventtypes,withspecific
F1scoresof0.71,0.51,and0.65forobstructiveapnea,centralapnea,and
hypopneaevents,respectively.Intwoindependent,multi-scoreddatasets,The
modelperformscomparablyorbetterthanindividualexpertraters.The
model’sprobabilisticoutput,termed“apnotyping,”providesinsightsinto
sleepdisorderedbreathingetiology,witheventprobabilitiescorrelatingmore
stronglywithkeysleepapneatraits—suchasloopgainandpharyngealmuscle
compensation—thantraditionalapneaindexes.Thisprobabilisticapproach
mayenhancediagnosticaccuracyandsupportpersonalizedtreatmentstra-
tegies,leadingtoimprovedpatientoutcomes.

Introduction
Sleep-related respiratory diseases, or sleep disordered breathing
(SDB),isanumbrellatermthatincludesmultipleconditions,ofwhich
centralsleepapnea(CSA)andobstructivesleepapnea(OSA)arethe
mostcommon1.ThemostusedmetricofCSAandOSAseverityisthe
apnea-hypopnea index (AHI). In this context, sleep “apneas” are
definedinadultsascompletecessationsofbreathinglastingatleast
10s (≥2 breaths), typically measured using a respiratory cannula
measuring inhalation and exhalation. Because respiratory effort is
concurrentlymeasuredusingchestandabdominalbelts,apneascan
be differentiated as centrally(CA) mediated (cessation of breathing
effort)orobstructive(OA).TheprimarydistinctionbetweenCSAand
OSAliesintheirunderlying mechanisms:CSAresults fromreduced
ventilation due to decreased central drive of breathing, while OSA
stemsfromupperairwayobstruction2.Inobstructiveevents,partial
airwaycollapseoftenoccurs,leadingtoreducedbreathingairflowwith
or without oxygen desaturation. These partial events are termed
hypopneas(HYPO),whicharedifficulttodifferentiateasobstructiveor
centrallymediated.

ThesystemforscoringSDBeventsiscomplex,subjective,andhas
beenextensivelyrevised.TheAmericanAcademyofSleepMedicine
(AASM)3hasestablishedcleardefinitionsfortwomaintypesofapnea
events,OAandCA.AASMdefinesOAas≥90%dropinairflowforat
least 10s with breathing effort shown in the thoracoabdominal
respiratorymovements.CAaredefinedbya≥90%dropincombined
flowamplitudesforatleast10swithoutrespiratoryeffort,shownasa
lackofthoracoabdominalexcursions3.Singlesleepapneaeventscan
have both central and obstructive components and are known as
“mixedapnea”events.Incomplexsleepapneacases,centralevents
typically emerge more clearly when treating obstructive-looking
eventswithPositiveAirwayPressure(PAP)therapy4.

ScoringinconsistenciesandevolvingHYPOdefinitionsoverthe
last 20 years contribute to discrepancies in OSA prevalence across
studies5–7.HYPOhasmultipledefinitions3,8andcanbedefinedaseither
a≥30%(AASM )or≥50%(priorto2012)dropincombinedampli-
2012
tudeofanairflowsignallastingatleast10sandproducingeithera≥3%
oxygen desaturation or/and an arousal. Centers for Medicare and
Medicaid Services (CMS) defines hypopneas as desaturation ≥4%
regardlessofthepresenceofarousals6,makingtheresultinghypopnea
index(HI)correlatehighlywiththeoxygendesaturationindex(ODI).
ScoringvariationacrosssitesandconstantevolutionofHYPOscoring
definition has contributed to discrepancies in OSA prevalence dis-
crepanciesacrossstudiesandlaboratories,andtoinconsistenciesin
the epidemiological literature5–7. In a recent epidemiological study,
60% ofmen and 30% ofwomen ≥40years old met criteria for sig-
nificantsleepapnea(AHI≥15/h)usingthemostrecentlyusedHYPO
criteria. Complicating the picture further, SDB events that do meet
criteriaforHYPO,OAorCAalsoexist.Forexample,breathingcessa-
tioncanpresentwithoutresultingarousalsordesaturations,denoted
isolatedrespiratoryeventswithoutarousalsordesaturations(IRE)in
thiswork,mereinstabilitiesinbreathing,whichcanbeobservedbut
are essentially not tabulated in clinical practice9,10. However, they
couldbeclinicallymeaningfulastheyareassociatedwithrespiratory
and heart rate changes, which can be reduced with optimal Con-
tinuousPAP(CPAP)treatment11.

Thecomplexityinscoringandhighvariabilitybetweenandwithin
scorershaveledsomeinvestigatorstoquestiontheAHIasameasure
ofSDBseverity12.Alternativemetrics,suchasthehypoxicburden,have
beenproposed.Thehypoxicburden,calculatedbysummingthearea
betweenthebaselineoxygensaturationlevelandtheoxygensatura-
tioncurvefordropsof3%ormoreassociatedwithrespiratoryevents,
hasbeensuggestedasapotentiallybettermeasureofSDBseveritythat
has been demonstrated to predict the incidence of cardiovascular
diseaseandmortality13–15.Whilethehypoxicburdeneffectivelyquan-
tifiesoxygendesaturation,itdoesnotcaptureSBDeventsthatoccur
withoutsignificantdropsinbloodoxygenlevels16,17.Thislimitationis
clinicallyimportant,asthesenon-desaturatingeventscanstillcause
sleep fragmentation and daytime symptoms. Individuals with these
events, usually young and likely characterized by a low arousal
threshold,maynotbeathighriskforchroniccardiovasculardisease18.
ThesesubjectsarelikelytohavetheirSDBeventsevolveintodesa-
turatingeventswithweightgainoraging16.Astheseeventsbecome
more frequent, chronic sleepiness increases, potentially raising the
arousal threshold and leading to longer events that do cause desa-
turation.Additionally,higherBody Mass Index(BMI)contributesto
SDBseveritynotonlybyexacerbatingupperairwayobstructionbut
also by potentially inducing hypoventilation due to the increased
effortrequired to breathe againsta heavier chestwall,further con-
tributingtoreducedventilationandoxygendesaturation19.

Consideringthesecomplexdefinitions,thereisaclearneedfor
revampingthedefinitionofSDBevents,ononehand,differentiating
central versus obstructive events, on another distinguishing events
with and without desaturations and/or arousals. Better definitions
based on pathophysiology and outcome variables are also needed.
Indeed, even just for obstructive events, multiple mechanisms are
involvedsothatsubjectswiththesameexact“passive’anatomy(i.e.,
theanatomicalconfigurationobservedunderconditionswithoutsig-
nificantmuscletone,suchasduringanesthesia)canhavevastlydif-
ferentindices20.Centralcontrolofbreathingplaysarole,ascentral
detectionofreducedventilationdirectlyaffectsbreathingfrequency
andrespiratoryeffort,aconceptcalledloopgain.Obstructiveevents
leadtobreathinginstability,whichinsomesubjects,suchasthosewith
highloopgain(subjectsreactingstronglytochangesincarbondiox-
ide),destabilizesbreathing,resultinginmoreevents.Thesesubjectsat
the extreme would have obstructive, central and mixed events. A
secondmechanisminvolvesprogressiveactivationoftheupperairway
dilatormuscle,whichcancontractandtoneuptoavoidcollapseas
negative pressure due to the obstruction and increased effort of
breathing occurs. These subjects may be more able to move their
tongueforwardandtenseuptheirairwaytoavoidcollapseduringa
significantpartofthenight.Thethirdmechanism,mentionedabove,is
theabilitytowakeuptointerrupttheeventbeforethedesaturation
occurs-these subjects are deemed to have a low arousal threshold
(younger subjects with arousal-only events are the extreme in this
category).Finally,eventscanbehighlypositional(worsesupine,asthe
tonguefallsbackward)orREMsleepdependent(becauseREMsleep
removes any muscle tone compensation). Although evaluation of
these phenotypes in individual patients mandates complex experi-
mentation,suchasputtingpatientsontherapeuticPAPandsuddenly
or progressively dropping pressure21, imperfect proxy measures of
loopgain(Lgn),arousalthreshold(ArT)andcompensatorypharyngeal
dilation muscle activation (Vcomp) have been developed based on
summary sleep study statistics22–26. These pathophysiological
mechanisms are not simply academic, as differential responses to
specifictherapiesshouldresult27,28.

Presumably due to the different definitions and heterogeneous
applicationthereof,severalstudiesshowlargeinter-andintra-scorer
variability in expert annotations of SDB events. A recent study has
demonstratedthatagreementonOA,CA,andHYPOeventsrangesfrom
0.52to0.778.Thisvariabilitypersistsevenwhenallowingfortemporal
flexibilityineventidentification(i.e.,presence/absenceofaneventwas
deemedconcordantwhenaneventwaspresentwithina30-swindow).
Several factors contribute to reliability. Subjectivity in determining
baselinebreathingamplitudeisaprimaryissue,asitiscrucialforesti-
matingthe30%dropinflowrequiredforhypopneaidentification.The
evaluationofhypoxemicepisodeamplitudealsovariesdependingon
theselectionofbaselineoxygensaturationvalues.Inconsistenciesin
arousalannotation,particularlyduringREMsleep,furthercomplicate
thescoringprocess.Expertfatigueduringlong,repetitivescoringses-
sionscanleadtoinconsistencies,ascan“cultural”biaseswithindiffer-
entsleeplaboratories.Changesineventdefinitionsovertimehavealso
contributedtoscoringinconsistencies.Finally,variationsinsignalfil-
teringcanaltertheappearanceofrecordings,notablyfornasalflow,
potentiallyaffectinghoweventsareinterpreted8,29.

As shown above, defining events, theirnature and theirpatho-
physiology remains challenging even in facility-based Type I PSGs
wheresleep,breathingflow,effortandothermeasuresaremeasured.
BecauseSDBissocommon,buttimeconsumingtoscoremanually,
studies have attempted to detect SDB automatically, using simpler
setups such as electrocardiogram (ECG)30–32, electroencephalogram
(EEG)33,orusingfullnightpolysomnography(PSG)withallrespiratory
channels32,34–41. Unfortunately, however, only a few of these studies
haveusedmorethanafewhundredsubjects32,36–41, and even fewer
haveattemptedtopredicteventtypes(OA,CAandHYPO)35,37,41.Fur-
ther,manystudieshaveonlytrainedtheirmodelsondatageneratedat
a single sleep center30–33,37,38,40,41, a problem known to decrease gen-
eralizabilityasshownbyOlesenetal.42.Therefore,thereremainsan
unmetneedforalarge-scalemulti-cohortmodelthatcandetectand
classifydifferenttypesofSDB,whilesimultaneouslyprovidingdata-
driven insights into the pathophysiology of SDB, which remains
complex.

In this study, we designed and validated an end-to-end deep
learning model, the Apneic Breathing Event Detector (ABED), for
detectingOA,CA,HYPO,andIREeventsinfull-nightPSGs.Weused
multipleindependentcohortsfortrainingandtesting.ABEDdetects
thelocationofeventspreciselyasanidealsleepscoringexpertwould.
Interestingly, comparing with established pathophysiological
measures,wealsoshowthatABEDprovidesinsightintothecauseof
SDBinindividualsubjects.Withthis,weaimtoincreasefocusonthe
definitionofsleepapneaandcreatearobust,readilyavailabledetec-
tionsystemthatcouldmakesleepapneadiagnosislesscostlyandmore
reliable,facilitatingtheinvestigationofsleepapneaonalargerscale.
Wealsoshowthattheprobabilitydistributionofeventtypewithineach
event,atermwecoin“apnotyping”,containsadditionalpathophysio-
logical information, showing that deep learning methods evaluating
sleep apnea offer the prospect of not only improving reliability but
couldalsobetterdefineSDBphenotyping,withpotentialdifferential
correlationswithoutcomesofinterest,suchascardiovasculardisease,
disturbedsleepandsleepiness43andpersonalizedtherapy.

Results
ABEDwastrainedon5456PSGsfromfourcohortsavailablethrough
theNationalSleepResearchResource(NSRR)44:theMulti-EthnicStudy
ofAtherosclerosis(MESA),theOsteoporoticFracturesinMenStudy
(MrOS),the Cleveland Family Study (CFS),and the WisconsinSleep
Cohort (WSC). For validation, 546 PSGs were reserved, while 1,099
PSGswereallocatedforfinaltesting,including249fromMESA,500
fromMrOS,250fromWSC,and100fromCFS.Additionalevaluation
wasperformedon113PSGsfromDREEMandAlliancedatasets,which
were scored by multiple experts. In these cohorts, there are three
HYPOscoringcriteria:therecommended(usedinMESA,MrOS,CFS,
andDREEM),thealternative(usedinAlliance),andthe4%desaturation
criteria(similartohowMedicaredefineshypopneasandusedinWSC).

ABED’s inputs include Nasal Flow, Naso-oral Thermistor,
AbdominalandThoracicPlethysmography,Blood/OxygenSaturation
(SpO2),andArousalandWakeProbabilities,asderivedfromourpre-
viously described multimodal arousal detector45. Pre-processing
involved applying a 4th-order digital Butterworth band-pass filter
(0.1–4Hz) to breathing channels (NasalFlow,Naso-oralThermistor,
AbdominalandThoracicPlethysmography)beforedownsamplingto
8Hz.Othermodalities(SpO2,ArousalandWakeProbabilities)were
upsampledto8Hz,andstandardizationwasappliedperPSG,except
forSpO2whichwasscaledtorangefrom0to1basedonsaturation
levelsof60%to100%,withSpO2below60%beingsetto0,andwake
andarousalprobabilitiesthatwerealreadyintherange0to1.ABED
utilizes a 240-s window with 50% overlap for detection. The archi-
tectureincorporateschannelmixing,featureextractionusingaflat-
tened ResNet50, bi-directional LSTM, and classification and
localizationlayers.Duringinference,non-maximumsuppressionand
classificationruleswereappliedtorefineeventpredictions.Anover-
viewoftheinputisshowninFig.1a,andthearchitectureisshownin
Fig. 1b. This approach allowed for a comprehensive detection and
classificationofobstructiveapneas(OA),centralapneas(CA),hypop-
neas (HYPO), and isolated respiratory events without arousals or
desaturations(IRE),providingarobusttoolforSDBanalysis.Tosim-
plify,anyannotatedmixedapneaswerelabeledasOAduetothere
beinganefforttobreatheduringtheevent.

SDBeventdetectionperformanceofABEDpercohortand
eventtype
Table1reportsonABEDclassificationmetricsF1,precision(P.),and
recall(R.)asshownforeachsinglescoredcohortandeventtype,along
withtheaverageintersectionoverunion(IOU).Metricsareshownfor
the detection of apnea events regardless of apnea event class (by
definition,IREsarenotconsideredapneaevents).InSupplementary
Table1weshowtheaverageofsimilarmetricsperPSG,andinSup-
plementaryFig.1theeventdetectionF1scoreisshownasafunctionof
thelog-transformednumberofevents,showingthatABEDhasabetter
detectionperformanceinPSGswithmanyevents.Figure1summarizes
thecharacteristicsofABED.Figure1ashowstheflowofdatafromPSG
into ABED, including EEG, EOG, and EMG through the multimodal
arousal detector (MAD)45 (represented as arousal and wake prob-
abilities),alongwiththerespiratorymodalities,nasalflow,naso-oral
thermistor,abdominalandthoracicbelt,andSpO2.ABEDreturnsthe
locationandtypeofthepredictedeventinthe240swindow,shownas
transparentboxes,withexpertannotationsshownasverticallines.In
thisexample,ABEDcorrectlyfindsthetypeandapproximatelocation
offourofsixannotatedevents,withoneOAoverlappingwithanearby
unpredictedCA,andoneCAismisclassifiedasOA.Figure1cpresentsa
confusionmatrixonABED’sdiagnosticaccuracyforsleepapneaonthe
fourseveritygroups(none(AHI<5/h);mild(5–15/h);moderate(15–30/
h);severe(>30/h))acrossthefoursingle-scoredcohortsMESA,MrOS,
WSC, and CFS. Additionally, ABED demonstrates 90.6% accuracy in
distinguishing central sleep apnea across severity classes. Figure 1d
presents a confusion matrix where each cell’s value represents the
fractionofeventsrelativetothetrueannotationswithineachevent
category.Figure1eshowsaregressionmodelofpredictedAHIversus
the annotated AHI with a correlation of 92% and a coefficient of
determinationof0.84.Theslopeoftheregressioncurveis0.90with
anintersectionof0.84.InSupplementaryTable2similarresultsfor
eachcohortinthetestsetareshown.InFig.1f,ABEDslatentspaceis
shownusing PHATE46 in200 randomly selected test PSGs(50each
fromMESA,MrOS,WSC,andCFS).Each240-swindowisplottedina
latentspacerepresentation,withwindowscontainingevents(colored)
and those without events (black). In the latent space, IRE (red) is
contiguous with HYPO (blue) and OA (green), while the rarer CA
(orange) forms a distinct subpopulation, almost orthogonal to the
contiguousIRE-HYPO-OAclusters.TheHYPO(blue)appearstoconsist
oftwoseparatesubpopulationsoneithersideofCA,creatinganearly
symmetricallatentspaceorthogonaltotheCAphenotype.Figure1g,h
presentsABEDperformanceintheunseen,multi-expertDREEM47and
Alliance8datasetscohortsbyclassincomparisonto4-5expertswhen
scoring 113 PSGs. ABED demonstrates equivalent or better perfor-
mancetoscorerswhenABEDisusedonthesenew,unseendatasets.In
theplot,theF1scoresofABEDs(blue)andexperts(red)arecompared
to the experts themselves, apart from the expert being tested. We
applied a two-tailed t-test to the F1 scores from experts and ABED
predictions to find out if there was any significant difference in
F1 scores. We found that ABED performed equal to or better than
expertsinallcases,withABEDperformingbetterin8of36compar-
isons,denotedbyastarontheexpertF1scoremedian.

We investigated the impact of signal quality, as determined by
metricsdirectlyobtainedfromtheNSRR,ontheF1-scorefordetecting
apneaevents.SignalqualitywasclassifiedasPoorbasedonthelowest
categorydefinedbytheNSRR.Ourfindingsindicatethatonlythoracic
andEEGsignalsclassifiedasPoorsignificantlyworsenedtheF1-score.
Conversely, factors such as an increase in age, AHI, and Asian race
positivelyinfluencedtheF1-score.Allsignificanteffectswereobserved
withp-valueslessthan0.05andcanbeseeninSupplementaryFig.2.

Apnotyping,probabilityofeacheventwithineachmajorityclass
anditsassociationwithendotypes
ABEDdoesnotonlyscoresandoutlinesthelocationofeachevent,but
it also outputs a probability for each event to belong to a specific
category;forexample,ifthehighestprobabilityisforOA,theeventwill
bescoredasOA,whilethesumoftheremainingeventprobabilities
canbeashighas0.49.Assuch,ABED’sprobabilityforeacheventalso
denoteshow“certain”thealgorithmisforthisassignment,andwith
whatothertypeofeventitisclosestto.BecauseCA,OA,HYPOandIRE
areacontinuum,theprobabilityofoneblendintothatofanothertype
ofevent,asillustratedinFig.2.Figure2aillustratesapnotyping,the
probability density of detected apneas per event, representing the
phenotype of each apnea event. In Fig. 2a, we compare subjects
exhibiting high fractions of different sleep-disordered breathing
events:CA(subject1fromthetop),OA(subject2fromthetop),HYPO
(subject 3 from the top), and IRE (subject 4 from the top). These
subjectsarearrangedfromhightolowontheloopgainspectrum.In
subjects 1 and 2, who have relatively high loop gain, we observe
multiple OA events with a relatively large CA-probability. Subject 2
specificallyshowsanIREeventwithhighOA-andCA-probabilityand
an OA event with high IRE-probability. Subjects 3 and 4, who have
relatively low loop gain, both primarily exhibit uniprobable events.
OnlyafewoftheHYPOandIREeventsintheselowloopgainsubjects
showsomenone-probability.

Sleep apnea pathophysiologyinvolves 1) passive anatomy,how
restrictedistheairwayintheabsenceofanyeffort,oftendefinedasthe
criticalclosingpressure,theminimalnegativepressurethatneedsto
occur to collapse the airway 2) loop gain (Lgn), a measure of the
respiratorysystem’ssensitivitytoCO₂ fluctuations anditsabilityto
stabilize breathing adjustments; 3) compensation, the ability of the
upper-airwaytoresistcollapsebydilatingtheairwaythroughthesti-
mulationofdilatormuscles(Vcom);4)arousalthreshold(Art),how
sensitivethesleeperistoacollapsingairway,resultinginanarousal.
Eachtraitisdefinedbyfluctuationsinventilationandventilatorydrive,
estimated using a chemoreflex model with least-squares regression.
Ventilatorydriveiscalculatedbyfittingafeedbackcontrolmodelto
the ventilation signal during open-airway breaths. Similarly, upper
airwaydilatorcompensationismeasuredbycomparingpeakventila-
tion during muscle activation at the arousal threshold with normal
ventilation.ThesemetricscorrelatewithtraditionalmethodslikePAP
pressuredropsandwerederivedfromtheMESAcohort48.Wethere-
forestudiedhowapnotypescorrelatewithphysiologicalsubtypesof
sleepapneaonthe249-subjectsubsetfromtheMESAstudy,reserved
fortestinginthiswork.Weappliedstepwiselinearmodelstopredict
loop gain, arousal threshold, and pharyngeal muscle compensation
using four groups of information: (1) demographics, including BMI,
age, and sex; (2) manually annotated indexes, such as the apnea-
hypopneaindex(AHI),obstructiveapneaindex(OAI),centralapnea
index(CAI),hypopneaindex(HI),andtheisolatedrespiratoryevents
withoutarousalsordesaturationsindex(IREI);(3)predictedindexes
matching the same categories; and (4) apnotyping, which included
predictedindexesandtheABEDaverageprobabilitiesofeachevent
giveneacheventforeachsubject.

Figure 2bpresents the resultsofthestepwise linear regression
models applied separately to four groups of information (demo-
graphics,annotations,predictionsandapnotyping)forthethreepre-
dictedvariables(Lgn,Vcomp,andArT),reportingtheiradjustedR2.All
models included demographics, and the results indicate that
apnotypingisthestrongestfeaturesetforpredictingloopgainand
pharyngealmusclecompensation,outperformingmanualannotation.
In contrast, manual annotations demonstrated stronger predictive
powerinthearousalthresholdpredictiontask,withapnotypingbeing
aclosesecond.Figure2c,illustratestheprobabilisticoverlapbetween
differentclasses,showingthatCAandOAsharethehighestprobability
overlap,followedbyOAwithHYPO,andthenHYPOwithIRE.These
patternsreflecttheirphysiologicalsimilaritiesandthemostcommonly
observedpathophysiologicaloverlaps.Figure2dpresentsaforestplot
displayingtheunivariateeffects of each information group (demo-
graphics, annotations, predictions, and apnotyping) on predicting
loopgain,arousalthreshold,and pharyngealmuscle compensation.
The multivariate effects are shown in Supplementary Table 4. Our
analysis highlights specific physiological correlations. For example,
highloopgainismoststronglypredictedbyahighisolatedrespiratory
eventindexandcentralapneaindex.Eventsclassifiedascentralapneas
withinIREsandIREswithinhypopneasappeartobeprotectiveforloop
gain.AhighprobabilityofIREswithinhypopneasisassociatedwitha
lower arousal threshold, whereas a high probability of hypopneas,
whether within hypopneas or obstructive apneas, correlates with a
higherarousalthreshold.Similarly,ahighOAprobabilitywithinIREsis
linked to an elevated arousal threshold. Interestingly, pharyngeal
muscle compensation shows a strong positive association with iso-
lated respiratory eventsand anegative association with obstructive
apnea index. A high probability of hypopneas within obstructive
apneassuggestslowpharyngealmusclecompensation.

ABEDincludesthedetectionofIREs,eventsthatsharefeatures
with HYPOs but are not associated with arousals or desaturations,
whicharerelatedtochangesinrespiratoryandheartratechanges11.
Thisclasshadtheweakestperformance,with22%ofIREsbeingmissed
completely. We included these events for two main reasons: first,
introducingIREswasexpectedtoimproveSAdiagnosisperformance
byprovidingaclassbetweenregularbreathingandHYPOs,potentially
reducing over-detection of HYPOs and improving AHI estimation.
Second, although IREs are seldom studied, they clearly occur in
cohortswherearousalandbreathingchannelswerescoredindepen-
dently, suggesting they aregenuine SDB events deserving ofstudy.
The inclusion of IRE scoring in ABED decreased the over-detection
ofHYPOs.

TheOAsaremostfrequentlymisclassifiedasHYPOs,andHYPOs
as IREs, reflecting their shared mechanisms of flow reduction and
progression as variations of the same obstructive phenomenon. In
contrast,fewHYPOsaremisclassifiedasCAs,aligningwiththerelative
rarity of central events. At the macro level, ABED’s predicted AHI
correlateshighlywithexpert-annotatedAHI(R²=0.84),withanalmost
perfect regression line (β=0.90) and limited bias (0.84 AHI). Fur-
thermore, ABED accurately determines severity in 75.3% of cases,
where very few cases are more than one severity group away. This
indicates that ABED can reliably diagnose sleep apnea with a fast
inferencetimeof14speraveragePSG,outperforminghumanexperts
inspeed.TheuseofABEDcouldultimatelyincreasediagnosisrates
andhelpalleviatetheeconomicburdencausedbyundiagnosedsleep
apnea49.

Methods
Datadescription
All data used in this study were collected under researchprotocols
approvedbythelocalinstitutionalreviewboards(IRB)ateachparti-
cipating institution, with written informed consent obtained from
individualspriortoparticipation.Specifically,MESAwasapprovedby
theIRBsofitssixfieldcenters(ColumbiaUniversity,JohnsHopkins
Medicine, Northwestern University, UCLA Office of the Human
Research Protection Program, University of Minnesota Human
ResearchProtectionProgram,andWakeForestBaptistHealth).CFS
was approved by the IRBs of Case Western Reserve University and
MassGeneralBrigham.MrOSreceivedapprovalfromtheIRBsofthe
KuakiniMedicalCenterandtheUniversityofHawai’iatMānoa.WSC
was approved by the University of Wisconsin–Madison Health Sci-
encesIRB.DREEMwasapprovedbytheStanfordUniversityIRB.Alli-
ancereceivedapprovalfromIRBsattheUniversityofPennsylvania,
HarvardUniversity,the UniversityofWisconsin–Madison,St. Luke’s
Hospital,andStanfordUniversity.

TotrainandevaluateABED,weused6555full-nightPSGsfrom
MESA (Multi-Ethnic Study of Atherosclerosis)72, MrOS (The Osteo-
poroticFracturesinMenStudy)73,WSC(WisconsinSleepCohort)74,and
CFS (Cleveland Family Study)75. These cohorts are available at the
NSRR(NationalSleepResearchResource)44.Wealsoincluded113PSGs
scoredbasedonfiveandfourexperts’annotations,datasetsthatwe
refertoasDREEM47andAlliance8,respectively,forevaluationonly.All
cohortscontainOA,CA,andHYPOannotations.ForMESAandMrOS,
weleveragedpartialbreathingcessationsthatwereannotatedinde-
pendently of desaturations and arousals. When these cessations
coincided with desaturations or arousals, wecreated HYPO annota-
tions.Theremainingunlinkedbreathingcessationswereusedtocre-
ate an additional class of breathing event denoted as isolated
respiratoryeventswithoutarousalsordesaturations(IRE),whichwere
usedduringtraining.ForWSCandCFS,weusedtheoriginalannota-
tionswithoutmodification.Anoverviewofdemographicsandsleep
apneastatisticsareshowninTable2.Sevenmodalitieswereusedas
input: Nasal Flow (NF), Naso-oral Thermistor (OT), Abdominal Ple-
thysmography(AbP),ThoracicPlethysmography(ThP),Blood/Oxygen
Saturation (SpO2), Arousal Probability (AP), and Wake Probability
(WP).NF,OT,AbP,ThP,andOSweredirectlyobtainedfromthePSG
whileAPandWPwereobtainedusingourpreviouslydescribedmul-
timodal arousal detector (MAD), which predicts arousals based on
centralEEG,rightandleftEOG,andEMG45.Allmodalitiesusedinthis
detectorareusedinthedefinitionofoneormoreoftheevents(OA,
CA,HYPO,andIRE)andarethereforehelpfulandimportantforSDB
eventsubtypedetection.

NF and OT measure a decrease in airflow, while AbP and ThP
indicateifthereisaconcomitantefforttobreathe.OSshowsblood
oxygendesaturationsthatoccurafterSDBeventonset(typicallywitha
30s delay).AP shows ifthereisanarousalfollowing thedecreased
airflowandistypicallyassociatedwitharesumptionofflow.Finally,as
SDBeventssuchasOA,CA,andHYPOcanonlyoccurduringsleep,WP
is important to distinguish irregular breathing during wakefulness
fromSDB.

Pre-processinganddetectionpipeline
Signalpreprocessingincludedbandpassfiltering(0.1–4Hz)andstan-
dardization of breathing channels, with all signals resampled to 8
Hz68,69. ABED processes 240-s windows with 50% overlap, using a
convolutionalneuralnetworktodetectandclassifySDBeventsasseen
inFig.1a.FollowingDOSED76,non-maximumsuppressionwasusedto
retrain only the most certain event detection77. The network archi-
tecturecombineschannelmixinglayers,featureextractionthrougha
modified ResNet5078 with batch normalization79 and ReLU80 activa-
tions, and bi-directional LSTM for temporal processing, both with
dropout81 to regularize. The specific architecture was chosen to
capture both local morphological patterns and long-range depen-
dencies in respiratory signals. The modified ResNet50 efficiently
extractslocalfeatures—suchasrespiratoryamplitudeandfrequency—
while the bi-directional LSTM models delayed relationships across
time—suchasthosebetweenflowlimitation,desaturation,andarousal
—crucial for distinguishing event types like HYPOs and IREs. More
details can be found in Appendix A3 sections Pre-Processing and
Detectionpipeline.

Training
ABEDwastrainedon5456PSGsacrossfourcohorts,with546PSGsfor
validation and 1099 for testing. Training used balanced sampling
across event classes and optimized both event classification and
localization. The model employs classification and localization loss
functionsrootedinthecrossentropyandHuber82loss,withAdam83
optimization and carefully tuned detection thresholds based on
F1 score for each event type. More details are present in Appendix
A3sectionsTraining,Networkoptimization,andDetectionthreshold.

Evaluation
Modelperformancewasassessedthroughprecision,recall,F1-score,
and IOU metrics. We evaluated ABED’s performance per event, per
PSG,andforAHI-baseddiagnosisclassification.Sleepapneaseverity
was categorized as: No SA (AHI<5), Mild (5≤AHI<15), Moderate
(15≤AHI<30), and Severe (AHI≥30). To see the full details on the
evaluations,seeAppendixA3,sectionEvaluation.

Endotype-apnotypinganalysis
WeperformedapnotypingusingABED,ananalysiswhereweexamine
the probability of each event type within each predicted event. To
evaluatethecapabilitiesofthisapproach,weappliedapnotypingtoa
stepwise linear regression model. We compared how ABED’s apno-
typing performed relative to baseline demographics and manual
annotationswhenusedtopredictsleepapneaendotypessuchasloop
gain(Lgn),arousalthreshold(ArT),andpharyngealmusclecompen-
sation(Vcomp).

Each endotypic trait is defined by spontaneous fluctuations in
ventilation (mean-normalized nasal pressure) and ventilatory drive
(intendedventilationestimatedusingachemoreflexmodelvialeast-
squares regression). For example, ventilatory drive is calculated by
inputtingtheventilationsignal—breath-to-breathtidalvolumemulti-
pliedbyrespiratoryrate,mean-normalized—intoachemoreflexfeed-
back control model characterized by parameters such as gain,
response time, and delay. This model outputs a ventilatory drive
signal,andparametersareadjustedtobestfitthedrivesignaltothe
ventilationsignalduringbreathswhentheairwayisopen(i.e.,venti-
lation between scored obstructive events) using least squares
regression.

Similarly, upper airway dilator compensation is defined as the
difference between ventilation at its peak muscle activation (at the
arousal threshold) and ventilation at normal (eupneic) ventilatory
drive.Thesemeasureshavebeenfoundtocorrelatewithmoretradi-
tionalmethodsthatusepositiveairwaypressure(PAP)pressuredrops.
Notably, these metrics have been derived from the MESA cohort48,
which is one of the cohorts included in our study, and 249 were
availablefromthetest-set.

In our endotype-apnotyping analysis, we employed a stepwise
linearregressionapproach.Thismethoditerativelyaddsvariableswith
thelowestp-valuegreaterthan0.01andremovesvariableswiththe
highestp-valueexceeding0.05fromthemodel,continuingthispro-
cessuntilnofurthervariablescanbeaddedorremoved.Theeffec-
tivenessofthestepwiselinearregressionmodelswasassessedusing
theadjustedR-squared(R²)coefficient.Thismeasureonlyincreasesif
an added variable significantly enhances the model’s explanatory
powerbeyondwhatwouldbeexpectedbychance.

We undertook feature engineering to construct variables used
withinourmodel,creatingmeasuresbasedontheaverageprobability
ofeacheventtypeforeachsubject.Fortheloopgaintest,detected
eventswerechosenbasedonthehighestprobabilityratherthanthe
heuristic described earlier. Specifically, we engineered a feature for
each event that encapsulates the probabilities of each event type
occurring within each event. We define these features, called apno-
types,asfollows:

Pð<eventtype i>j<eventtype j>Þ

This represents the average probability of event type i given
eventtypej.

Finally,wepresentthelinearcorrelationsofourapnotypeswith
eachoftheendotypingmeasures—loopgain(Lgn),arousalthreshold
(ArT),andpharyngealmusclecompensation(Vcomp)—whilecontrol-
lingfortheeffectsofage,bodymassindex(BMI),andsex.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study focuses on detecting sleep apnea-related respiratory events through the use of a deep learning approach and up to 7 PSG overnight signals (plus data from EEG, EOG, and EMG). The topic is of interest for sleep research, the objectives are well-proposed, and the manuscript is well-written. The authors establish that their approach is useful in sleep apnea detection, mainly because of its ability to properly differentiate the several types of respiratory events (including a new event type), thus covering a gap in the related literature. Despite the interest of the proposal, it is my opinion that several major and minor issues should be addressed prior to the publication of the manuscript:\nMajor issues:\n1/ Apneic event length is 10 seconds or more. Therefore, the frequency associated with their occurrence is below 0.1 Hz. Accordingly, respiratory signals being filtered between 0.1 Hz and 4 Hz are losing very important information. Several studies report that the most useful sleep apnea-related information in respiratory signals is below 0.1 Hz. It is my opinion that filtering should be changed to between 0.025 Hz and 4 Hz to improve the performance of the model, which is high but not impressive.\n2/ I understand the advantages of the current proposal regarding previous approaches. However, it is my opinion that the manuscript fails to compare the diagnostic performance (not the scoring performance) with that reached by many other proposals. There exist previous studies using thousands of subjects but only using single channels like SpO2 that reach diagnostic performances that cannot be seen as inferior to those reported here using 7 + 3 signals. As diagnosis is the major conceptual goal of these automated approaches, the authors should compare and discuss these results with other relevant proposals in this particular task.\n3/ As there are deep learning solutions that do not require an equal sample rate for the signals, and 8 Hz is below the sampling rate recommended by the AASM for respiratory signals, this choice should be justified and acknowledged as a limitation of the study.\n4/ I think that the choice of the particular deep learning architecture should be justified and discussed. The existence of literature pointing to CNNs as reaching higher performances than RNNs, even in tasks involving signal processing (time series), justifies clearly explaining the reasons behind the choice of the deep learning architecture (which in this case includes a combination of both approaches via ResNet).\n5/ The intra-class correlation coefficient is better suited than Pearson’s correlation to measure agreement between different scorers (with the predicted AHI being one of them).\nMinor issues:\n- \"To simplify any annotated mixed apneas were labelled as OA due to there being an effort to breathe during the event.\" It is my opinion that this is not a simplification but a limitation (probably due to an unavoidable class imbalance concerning this type of event)", "reviewer_2": "The authors present an assessment of a new automatic sleep disordered breathing event detector (ABED) for identifying respiratory events in sleep disordered breathing. They report that generally the ABED performs as well as individual expert scores, particularly for obstructive apneic events. Data from a number of different, existing cohorts were used to train and validate the ABED, which is a strength of the study.\nThe potential of this is that the very rapid inference time (14 seconds per average PSG is cited) can increase efficiency and scope of sleep apnea testing.\nA number of possible areas for clinical translation are given. I think the most attractive is in easily identifying endotypes of obstructive sleep apnea. Endotypic traits have been proposed for use in selecting patients that might benefit preferentially from a range of non-CPAP alternative therapies including oral appliances, hypoglossal nerve stimulation, supplemental oxygen, and pharmacotherapy. Given there is currently a one-size-fits-all management approach that predominates where CPAP is first line treatment for almost all patients. Accurately identifying patients likely to respond to alternatives to CPAP to avoid a trial and error approach to alternatives would be a major advance. Systems to identify endotypes from routine PSG signals with automated modelling have been developed and some are referenced here (21-25). However, the authors have not referenced further work that has demonstrated some of these systems are highly accurate in predicting, for example, oral appliance treatment outcomes in obstructive sleep apnea (see Dutta et al, J Clin Sleep Med 2022), or that combine clinical data and models from PSG data (see Eckert et al, Sleep 2024;47 (Suppl 1): A257) to identify successful candidates for Oral Appliance Therapy (and vice versa). The authors assert that the addition of “apnotyping” via probabilistic detection of event subtypes within each majority class could benefit sleep apnea endotyping. It remains an open question whether the ABED will be as effective as these methods in clinical practice as this should be acknowledged in the limitations. Further research is also needed to demonstrate if endotypes are useful in predicting which patients are at risk of adverse health consequences of OSA. This is beyond the scope of the current study.\nThe authors also do not reference previous work on the significance of isolated respiratory events without arousals or desaturations (IRE). Previous work has shown non-apneic REs without cortical arousal or desaturation are associated with significant respiratory and heart rate changes. And, that optimal CPAP and the reduction of resistive load are associated with the normalization of heart rate indicating potential clinical benefit (for example see Sankari et al, Sleep 2016 ;40:zsw024). Easy identification of these events would be useful but is not a novel finding.\nThere are a number of other limitations I feel need to be considered in addition to those given in the Discussion. As the authors point out in the Introduction, a number of novel metrics taken from the PSG have been identified that have shown to be better predictors of downstream consequences of OSA than the AHI. These include hypoxic burden and a number of metrics derived from heart rates. Further, the intensity of arousals from events are also likely to be clinically significant. It does not appear that the ABED identifies any of these metrics. Consequently, the value of the ABED in identifying which patients may benefit long-term from therapy is uncertain. Sleep studies also require scoring of sleep stages and identification of arousals due to limb movements or that occur spontaneously. I presume the ABED would need to be utilised in addition to automatic scoring software for these features.\nOverall, the paper presents a potentially useful addition to automate the scoring of sleep-disordered breathing events that will likely be of value in sleep medicine."}, "author_response": "We thank the reviewers for their valuable suggestions. The following response will be formatted with Reviewer comments in blue and Responses in black.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study focuses on detecting sleep apnea-related respiratory events through the use\nof a deep learning approach and up to 7 PSG overnight signals (plus data from EEG,\nEOG, and EMG). The topic is of interest for sleep research, the objectives are well-\nproposed, and the manuscript is well-written. The authors establish that their approach\nis useful in sleep apnea detection, mainly because of its ability to properly differentiate\nthe several types of respiratory events (including a new event type), thus covering a gap\nin the related literature. Despite the interest of the proposal, it is my opinion that several\nmajor and minor issues should be addressed prior to the publication of the manuscript:\nMajor issues:\n1/ Apneic event length is 10 seconds or more. Therefore, the frequency associated with\ntheir occurrence is below 0.1 Hz. Accordingly, respiratory signals being filtered between\n0.1 Hz and 4 Hz are losing very important information. Several studies report that the\nmost useful sleep apnea-related information in respiratory signals is below 0.1 Hz. It is\nmy opinion that filtering should be changed to between 0.025 Hz and 4 Hz to improve\nthe performance of the model, which is high but not impressive.\nWe thank the reviewer for this thoughtful and important comment regarding the low-\nfrequency content of respiratory signals.\nWe would like to clarify that although apneic events typically last >= 10 seconds, their\nphysiological signatures - particularly reductions in airflow or effort - are reflected in\nmodulations of the signal envelope rather than the generation of new frequency\ncomponents below 0.1 Hz. In our experience, these envelope changes remain\ndetectable even after applying a 0.1 Hz high-pass filter, since the base breathing\nfrequencies (typically ~0.2–0.4 Hz) are still present.\nTo empirically assess the reviewer’s suggestion, we retrained and evaluated our model\nusing a 0.025-4 Hz band-pass filter and compared the results to our original 0.1-4 Hz\nfiltering. The results across four cohorts (MESA, MrOS, WSC, CFS) show minimal and\ninconsistent performance differences. As shown in rebuttal tables 1 and 2, there is no\nconsistent improvement when including frequencies below 0.1 Hz; in fact, for some\nevent types (e.g., HYPO per event, OA per recording), performance is higher with the\n0.1 Hz cutoff.\nRebuttal table 1: Per event across all PSGs:\n0.025 - 4 Hz 0.1 - 4 Hz\nF1 score\nOA 0.73 0.71\nCA 0.52 0.51\nHYPO 0.55 0.65\nIRE 0.53 0.47\nRebuttal table 2: Per event mean by PSG:\n0.025 - 4 Hz 0.1 - 4 Hz\nF1 score\nOA 0.52 0.57\nCA 0.51 0.51\nHYPO 0.60 0.55\nIRE 0.49 0.44\nWe also include Rebuttal Figure 1, which shows filtered (green = our chosen filter,\norange = filter proposed by reviewer) vs. unfiltered (blue) examples of respiratory effort\nsignals during two apneic events separated by recovery breaths.\nThe high similarity across traces between 0.025-4 Hz and 0.1-4 Hz filters suggests that\nthe clinically relevant apnea signatures remain intact. In Rebuttal Figure 2 we see the\nspectrogram of the recording and the attenuation of the signal below 0.1 Hz for the\norange signal is prior to the first peak\nRebuttal Figure 1: Time series representation of respiratory signals with different filter\nsettings. Blue: unfiltered, Green: original filtering (bandpass: 0.1 - 4hz), Orange:\nproposed changed filtering (bandpass: 0.025 - 4hz)\nRebuttal Figure 2: The power spectrum of signals with different filter settings. Blue: unfiltered, Green: original filtering (bandpass: 0.1 - 4hz), Orange: proposed changed filtering (bandpass: 0.025 - 4hz)\nFinally, we note that the MrOS study applied a hardware high-pass filter at 0.15 Hz to\nmost respiratory channels (except nasal flow), whereas MESA and CFS did not apply\nsuch filtering. To harmonize preprocessing across cohorts and avoid cohort-specific\nbias in model training, we chose a conservative high-pass cutoff of 0.1 Hz. This tradeoff\nsupports generalization while still preserving the envelope information necessary for\napnea detection and has minimal influence on results across populations.\n2/ I understand the advantages of the current proposal regarding previous approaches.\nHowever, it is my opinion that the manuscript fails to compare the diagnostic\nperformance (not the scoring performance) with that reached by many other proposals.\nThere exist previous studies using thousands of subjects but only using single channels\nlike SpO2 that reach diagnostic performances that cannot be seen as inferior to those\nreported here using 7 + 3 signals. As diagnosis is the major conceptual goal of these\nautomated approaches, the authors should compare and discuss these results with\nother relevant proposals in this particular task.\nWe thank the reviewer for raising this important point regarding diagnostic performance.\nWe agree that comparing our model’s diagnostic utility with simpler approaches is\nnecessary to contextualize its relevance and impact.\nIndeed, previous studies have demonstrated strong diagnostic performance using\nsingle-channel inputs such as SpO2 [https://www.nature.com/articles/s41467-023-\n40604-3], achieving high ICC and F1 metrics for AHI estimation at (ICC: 0.92, F1: 0.78),\n(ICC: 0.94, F1: 0.74), and (ICC: 0.94, F1: 0.75) for CFS, MrOS, and MESA,\nrespectively. Comparatively, we achieve ICC = 0.92 and F1 = 0.74. These models\nbenefit from ease of use and scalability, and we agree that their contributions are\nvaluable - especially in population-level screening contexts.\nIn contrast, our approach is explicitly designed to go beyond estimation of AHI, which\nhave been increasingly criticized as insufficient for characterizing apnea severity and\nguiding personalized treatment. Our model leverages multichannel PSG data to provide\nevent-by-event classification, differentiation of apnea events (obstructive, central,\nhypopnea, and isolated respiratory events), and the ability to inform apnea endotyping.\nRather than only predicting whether a patient exceeds an AHI threshold, our model\ncharacterizes the temporal structure and type of respiratory disturbances, which are\nmore closely associated with physiological burden and clinical outcomes. Several\nstudies emphasize the importance of phenotyping sleep apnea patients beyond the AHI\n- for instance, identifying arousability, hypoxic burden, or event clustering - which are\nnot easily captured by single-channel or threshold-based methods.\nWe have added a discussion of this comparison to the revised manuscript, highlighting\nthe trade-offs between simplicity and diagnostic richness. Ultimately, we see our work\nas complementary: while single-channel systems are suitable for screening, detailed\nmulti-channel models like ours offer higher resolution insights needed for personalized\ninterventions and advancing our understanding of sleep apnea pathophysiology.\n3/ As there are deep learning solutions that do not require an equal sample rate for the\nsignals, and 8 Hz is below the sampling rate recommended by the AASM for respiratory\nsignals, this choice should be justified and acknowledged as a limitation of the study.\nWe thank the reviewer for this insightful comment. We have now added both the\nrationale for our choice and a corresponding limitation statement in the revised\nmanuscript.\nOur choice of an 8 Hz sampling rate reflects a trade-off between computational\nefficiency and retaining signal information across a large, multi-cohort dataset. This\nsampling rate is sufficient to capture the primary respiratory dynamics relevant to sleep\napnea detection, which predominantly occur in the 0.2-0.33 Hz range, corresponding to\nnormal adult breathing rates (≈ 12-20 breaths per minute)\n[https://pubmed.ncbi.nlm.nih.gov/8774258/]. For thoracic, abdominal, and oral\nthermistor signals - where respiratory effort and airflow patterns are relatively slow - 8\nHz ensures adequate resolution while minimizing training costs. For nasal airflow, we\nnote that although certain high-frequency components like snoring vibrations occur >\nsnoring acoustics, and the 8 Hz rate captures the signal envelope adequately for the\npurposes of apnea event classification.\nHowever, we acknowledge the reviewer’s point that the AASM recommends higher\nsampling rates (>= 25 Hz) for respiratory channels to ensure accurate waveform\nmorphology, particularly in clinical diagnostic settings. While our approach performs well\nat 8 Hz, this limitation should be considered in contexts where finer temporal resolution\nmay be required, especially for detecting subtle respiratory patterns or developing\nmodels for real-time clinical applications.\nWe now acknowledge this as a limitation in the manuscript and recommend that future\nwork explore whether using higher sampling rates could further enhance model\nperformance, particularly in prospective datasets.\n4/ I think that the choice of the particular deep learning architecture should be justified\nand discussed. The existence of literature pointing to CNNs as reaching higher\nperformances than RNNs, even in tasks involving signal processing (time series),\njustifies clearly explaining the reasons behind the choice of the deep learning\narchitecture (which in this case includes a combination of both approaches via ResNet).\nWe thank the reviewer for the helpful comment encouraging a deeper justification of our\nmodel architecture which we have added to the Manuscript. The ABED architecture was\ndesigned to leverage both spatial and temporal signal characteristics through a hybrid\nstructure that combines a convolutional feature extractor (a modified ResNet50) with a\ntemporal modeling layer (bi-directional LSTM). This design reflects the nature of the\ninput data—multimodal time series from respiratory, oxygen saturation, and arousal-\nrelated signals—where both short-term local features and longer-range dependencies\nare important.\nConvolutional neural networks (CNNs), particularly residual networks (ResNets), are\nknown for their ability to efficiently learn spatial hierarchies in signal patterns and are\nless prone to overfitting compared to simpler CNNs or multilayer perceptrons (MLPs),\nespecially when deep architectures are used thanks to residual connections [He et al.,\n2016, Ref. 69]. We selected ResNet due to its scalability, stable gradient flow, and\ndemonstrated success in prior applications [He et al., 2016, Ref. 69].\nWhile some studies suggest that CNNs alone can outperform recurrent models for time\nseries tasks [Van Steenkiste et al., 2019, Ref. 31], these often rely on single-modality\ninput or short temporal contexts. In contrast, sleep apnea involves complex temporal\ndependencies - such as the delayed relationship between respiratory flow reduction,\noxygen desaturation, and arousal - that are difficult to capture without explicit sequence\nmodeling. The distinction between a hypopnea and an isolated respiratory event (IRE) relies not just on morphology, but on when these features co-occur. Therefore, temporal\nmodeling is critical.\nTo that end, we employed a bi-directional LSTM to capture both forward and backward\ntemporal dependencies within 240-second windows, allowing the model to integrate\ncontext across time and across channels. This is essential in sleep breathing analysis,\nwhere the onset and consequence of an event may be separated by several tens of\nseconds. We also considered transformer-based models but found them less suitable\nfor our mid-scale dataset due to their high parameter count and increased risk of\noverfitting without massive datasets.\n5/ The intra-class correlation coefficient is better suited than Pearson’s correlation to\nmeasure agreement between different scorers (with the predicted AHI being one of\nthem).\nWe thank the reviewer for this insightful suggestion. We agree that the intra-class\ncorrelation coefficient provides a more appropriate measure of agreement between true\nand predicted AHI values as it also takes into account the bias of the model. We have\ncalculated the ICC of true and predicted AHI and get ICC = 0.92(95% CI: [0.90 0.93]).\nWe have now added these to the revised manuscript in Figure 1e.\nMinor issues:\n- \"To simplify any annotated mixed apneas were labelled as OA due to there being an\neffort to breathe during the event.\" It is my opinion that this is not a simplification but a\nlimitation (probably due to an unavoidable class imbalance concerning this type of\nevent)\nWe thank the reviewer for this observation and agree that the handling of mixed apneas\nrepresents a limitation rather than a simplification. As noted, the primary reason for this\ndecision was the significant class imbalance: mixed apneas constituted a very small\nfraction of events across all cohorts, making it challenging to train a robust classifier for\nthis category. Mixed apneas were labeled as OA due to the partial presence of\nrespiratory effort.\nHowever, we acknowledge that mixed apneas may have distinct pathophysiological\nsignificance and that their labeling as OA reduces diagnostic granularity. We have\nupdated the limitations section of the manuscript to reflect this point.\nReviewer #2 (Remarks to the Author):\nWe thank the reviewer for their insightful comments and suggestions.\nThe authors present an assessment of a new automatic sleep disordered breathing\nevent detector (ABED) for identifying respiratory events in sleep disordered breathing.\nThey report that generally the ABED performs as well as individual expert scores,\nparticularly for obstructive apneic events. Data from a number of different, existing\ncohorts were used to train and validate the ABED, which is a strength of the study.\nThe potential of this is that the very rapid inference time (14 seconds per average PSG\nis cited) can increase efficiency and scope of sleep apnea testing.\nA number of possible areas for clinical translation are given. I think the most attractive is\nin easily identifying endotypes of obstructive sleep apnea. Endotypic traits have been\nproposed for use in selecting patients that might benefit preferentially from a range of\nnon-CPAP alternative therapies including oral appliances, hypoglossal nerve\nstimulation, supplemental oxygen, and pharmacotherapy. Given there is currently a\none-size-fits-all management approach that predominates where CPAP is first line\ntreatment for almost all patients. Accurately identifying patients likely to respond to\nalternatives to CPAP to avoid a trial and error approach to alternatives would be a major\nadvance. Systems to identify endotypes from routine PSG signals with automated\nmodelling have been developed and some are referenced here (21-25). However, the\nauthors have not referenced further work that has demonstrated some of these systems\nare highly accurate in predicting, for example, oral appliance treatment outcomes in\nobstructive sleep apnea (see Dutta et al, J Clin Sleep Med 2022), or that combine\nclinical data and models from PSG data (see Eckert et al, Sleep 2024;47 (Suppl 1):\nA257) to identify successful candidates for Oral Appliance Therapy (and vice versa).\nWe thank the reviewer for highlighting the clinical relevance of endotype-based\ntreatment selection and for suggesting key references that strengthen the translational\npotential of our work. In our revised manuscript, we have incorporated the study by\nDutta et al. (2022) [https://jcsm.aasm.org/doi/full/10.5664/jcsm.9742], which developed\na physiological-based model using standard polysomnography and clinical data to\npredict oral appliance treatment outcomes in obstructive sleep apnea. This study\nreported a mean accuracy of 91% ± 8% using 10-fold cross-validation and 100%\naccuracy in an independent validation set for predicting responders (AHI < 5 events/h)\nPMC. We have also referenced the work by Eckert et al. (2024)\n[https://academic.oup.com/sleep/article/47/Supplement_1/A257/7654650?login=true],\nwhich combines clinical data and PSG models to identify successful candidates for oral\nappliance therapy. These additions provide a more comprehensive overview of existing\npredictive models in the field.\nThe authors assert that the addition of “apnotyping” via probabilistic detection of event\nsubtypes within each majority class could benefit sleep apnea endotyping. It remains an\nopen question whether the ABED will be as effective as these methods in clinical\npractice as this should be acknowledged in the limitations. Further research is also\nneeded to demonstrate if endotypes are useful in predicting which patients are at risk of\nadverse health consequences of OSA. This is beyond the scope of the current study.\nWe acknowledge that while the ABED demonstrates promising performance in\ndetecting respiratory events and subtypes, its effectiveness in predicting treatment\noutcomes, such as response to oral appliance therapy, remains to be established. In the\nrevised manuscript, we have added a statement in the limitations section to reflect this,\nnoting that further research is needed to compare the ABED's predictive capabilities\nwith existing models and to validate its clinical utility in diverse patient populations.\nThe authors also do not reference previous work on the significance of isolated\nrespiratory events without arousals or desaturations (IRE). Previous work has shown\nnon-apneic REs without cortical arousal or desaturation are associated with significant\nrespiratory and heart rate changes. And, that optimal CPAP and the reduction of\nresistive load are associated with the normalization of heart rate indicating potential\nclinical benefit (for example see Sankari et al, Sleep 2016 ;40:zsw024). Easy\nidentification of these events would be useful but is not a novel finding.\nThank you for highlighting this omission. We have now included a reference to the study\nby Sankari et al. (2016), which demonstrated that non-apneic respiratory events without\ncortical arousal or desaturation are associated with significant respiratory and heart rate\nchanges, and that optimal CPAP therapy can normalize heart rate, indicating potential\nclinical benefit. This addition acknowledges the clinical significance of IREs and situates\nour work within the broader context of sleep-disordered breathing research.\nThere are a number of other limitations I feel need to be considered in addition to those\ngiven in the Discussion. As the authors point out in the Introduction, a number of novel\nmetrics taken from the PSG have been identified that have shown to be better\npredictors of downstream consequences of OSA than the AHI. These include hypoxic\nburden and a number of metrics derived from heart rates. Further, the intensity of\narousals from events are also likely to be clinically significant. It does not appear that\nthe ABED identifies any of these metrics. Consequently, the value of the ABED in\nidentifying which patients may benefit long-term from therapy is uncertain. Sleep studies\nalso require scoring of sleep stages and identification of arousals due to limb\nmovements or that occur spontaneously. I presume the ABED would need to be utilised\nin addition to automatic scoring software for these features.\nWe appreciate the reviewer’s insights into additional limitations. In the revised\nmanuscript, we have expanded the limitations section to discuss the following points:\n● Novel Metrics: Although ABED describes the overall apnotypes of subjects, we\nacknowledge that the ABED does not identify certain novel metrics derived from\nPSG, such as hypoxic burden and heart rate variability, which have been shown\nto be better predictors of downstream consequences of OSA than the AHI\n[https://pmc.ncbi.nlm.nih.gov/articles/PMC8483223/?,\nhttps://academic.oup.com/eurheartj/article/40/14/1149/5146754].\n● Comprehensive Sleep Study Scoring: We recognize that sleep studies require\nscoring of sleep stages and identification of arousals due to limb movements or\nspontaneous occurrences. The ABED is designed to detect respiratory events\nand would need to be utilized in conjunction with automated scoring tools for full\nPSG interpretation.\nThese acknowledgments provide a more balanced perspective on the capabilities and\ncurrent limitations of the ABED system."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my concerns", "reviewer_2": "No further comments"}, "author_response": ""}]

Decision letter