0421 Detecting Central Sleep Apnea Using a Multi-Diagnostic Chest-Worn Monitor Article Swipe
Rami Khayat
,
Kara Dupuy-McCauley
,
Behnam Molavi
,
Surina Sharma
,
Nancy A. Collop
,
Cathy Goldstein
,
Ilene M. Rosen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsaf090.0421
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/sleep/zsaf090.0421
Introduction Identifying central sleep apnea (CSA) during home sleep apnea testing is important to guide appropriate treatment for sleep-disordered breathing (SDB) and underlying cardiovascular conditions, including heart failure and atrial fibrillation. SANSA (Huxley Medical, Inc.) is a multi-diagnostic chest-worn monitor capable of detecting SDB, cardiac arrhythmias, and heart function. Using polysomnography (PSG) as a reference, we developed a machine learning (ML) method to classify respiratory events from this monitor as central or obstructive. Methods Data was analyzed from ninety-five subjects with suspected SDB who wore the chest monitor simultaneously with PSG overnight. To address known scoring heterogeneity of CSA, PSG records were scored for central apneas by two core laboratories (Core 1 and Core 2). Core 1 annotations from twenty-eight records were first used to train a random forest classifier to differentiate obstructive (N=680) and central events (N=388). The model used oximetry, respiratory effort, and snoring to classify events based on effort, duration, and other features. A five-fold cross validation was used to evaluate event-level performance. The model was then used to estimate subject-level CAI for all subjects and compared with Core 2 scores. Results Algorithm event-level sensitivity and specificity to detect central events was 72.7% and 81.0%. Subject-level CAI for all records between the chest monitor and Core 2 had a correlation coefficient of 0.90 and root mean squared error (RMSE) of 4.6 events/hour, versus 0.82 and 4.7 events/hour comparing Core 1 and Core 2. Sensitivity and specificity to detect CAI≥10 events/hour was 80.0% (4/5) and 98.9% (89/90), compared to 100% (3/3) and 98.9% (91/92) for CAI≥15. Conclusion An ML algorithm to differentiate obstructive and central events collected from the chest-worn monitor demonstrated favorable event-level performance and comparable CAI agreement to that between two PSG core laboratories. Home-based CSA detection alongside cardiac monitoring could identify high risk patients for expedited therapy, cardiology referral, or follow-up PSG. These results motivate further validation in a larger cohort as well as investigation of outcomes related to time to evaluation and management. Support (if any) National Science Foundation; Georgia Research Alliance; Huxley Medical, Inc.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/sleep/zsaf090.0421
- https://academic.oup.com/sleep/article-pdf/48/Supplement_1/A185/63221035/zsaf090.0421.pdf
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410502868
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410502868Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/sleep/zsaf090.0421Digital Object Identifier
- Title
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0421 Detecting Central Sleep Apnea Using a Multi-Diagnostic Chest-Worn MonitorWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-01Full publication date if available
- Authors
-
Rami Khayat, Kara Dupuy-McCauley, Behnam Molavi, Surina Sharma, Nancy A. Collop, Cathy Goldstein, Ilene M. RosenList of authors in order
- Landing page
-
https://doi.org/10.1093/sleep/zsaf090.0421Publisher landing page
- PDF URL
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https://academic.oup.com/sleep/article-pdf/48/Supplement_1/A185/63221035/zsaf090.0421.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/sleep/article-pdf/48/Supplement_1/A185/63221035/zsaf090.0421.pdfDirect OA link when available
- Concepts
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Central sleep apnea, Medicine, Sleep (system call), Sleep apnea, Polysomnography, Apnea, Cardiology, Internal medicine, Computer science, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.PSG | 91, 100, 286 |
| abstract_inverted_index.SDB | 83 |
| abstract_inverted_index.The | 139, 167 |
| abstract_inverted_index.all | 177, 202 |
| abstract_inverted_index.and | 22, 29, 47, 113, 135, 145, 154, 179, 189, 197, 208, 217, 228, 234, 238, 247, 254, 266, 278, 328 |
| abstract_inverted_index.for | 18, 104, 176, 201, 257, 300 |
| abstract_inverted_index.had | 211 |
| abstract_inverted_index.the | 86, 205, 271 |
| abstract_inverted_index.two | 108, 285 |
| abstract_inverted_index.was | 76, 161, 169, 195, 244 |
| abstract_inverted_index.who | 84 |
| abstract_inverted_index.(ML) | 61 |
| abstract_inverted_index.0.82 | 227 |
| abstract_inverted_index.0.90 | 216 |
| abstract_inverted_index.100% | 252 |
| abstract_inverted_index.CSA, | 99 |
| abstract_inverted_index.Core | 114, 116, 182, 209, 232, 235 |
| abstract_inverted_index.Data | 75 |
| abstract_inverted_index.Inc. | 341 |
| abstract_inverted_index.PSG. | 307 |
| abstract_inverted_index.SDB, | 44 |
| abstract_inverted_index.any) | 332 |
| abstract_inverted_index.core | 109, 287 |
| abstract_inverted_index.from | 67, 78, 119, 270 |
| abstract_inverted_index.high | 297 |
| abstract_inverted_index.home | 8 |
| abstract_inverted_index.mean | 219 |
| abstract_inverted_index.risk | 298 |
| abstract_inverted_index.root | 218 |
| abstract_inverted_index.that | 283 |
| abstract_inverted_index.then | 170 |
| abstract_inverted_index.this | 68 |
| abstract_inverted_index.time | 325 |
| abstract_inverted_index.used | 124, 141, 162, 171 |
| abstract_inverted_index.well | 318 |
| abstract_inverted_index.were | 102, 122 |
| abstract_inverted_index.with | 81, 90, 181 |
| abstract_inverted_index.wore | 85 |
| abstract_inverted_index.(3/3) | 253 |
| abstract_inverted_index.(4/5) | 246 |
| abstract_inverted_index.(CSA) | 6 |
| abstract_inverted_index.(Core | 111 |
| abstract_inverted_index.(PSG) | 52 |
| abstract_inverted_index.(SDB) | 21 |
| abstract_inverted_index.72.7% | 196 |
| abstract_inverted_index.80.0% | 245 |
| abstract_inverted_index.98.9% | 248, 255 |
| abstract_inverted_index.Inc.) | 35 |
| abstract_inverted_index.SANSA | 32 |
| abstract_inverted_index.These | 308 |
| abstract_inverted_index.Using | 50 |
| abstract_inverted_index.apnea | 5, 10 |
| abstract_inverted_index.based | 150 |
| abstract_inverted_index.chest | 87, 206 |
| abstract_inverted_index.could | 295 |
| abstract_inverted_index.cross | 159 |
| abstract_inverted_index.error | 221 |
| abstract_inverted_index.first | 123 |
| abstract_inverted_index.guide | 15 |
| abstract_inverted_index.heart | 27, 48 |
| abstract_inverted_index.known | 95 |
| abstract_inverted_index.model | 140, 168 |
| abstract_inverted_index.other | 155 |
| abstract_inverted_index.sleep | 4, 9 |
| abstract_inverted_index.train | 126 |
| abstract_inverted_index.(RMSE) | 222 |
| abstract_inverted_index.81.0%. | 198 |
| abstract_inverted_index.Huxley | 339 |
| abstract_inverted_index.apneas | 106 |
| abstract_inverted_index.atrial | 30 |
| abstract_inverted_index.cohort | 316 |
| abstract_inverted_index.detect | 192, 241 |
| abstract_inverted_index.during | 7 |
| abstract_inverted_index.events | 66, 137, 149, 194, 268 |
| abstract_inverted_index.forest | 129 |
| abstract_inverted_index.larger | 315 |
| abstract_inverted_index.method | 62 |
| abstract_inverted_index.random | 128 |
| abstract_inverted_index.scored | 103 |
| abstract_inverted_index.versus | 226 |
| abstract_inverted_index.(91/92) | 256 |
| abstract_inverted_index.(Huxley | 33 |
| abstract_inverted_index.(N=680) | 134 |
| abstract_inverted_index.Georgia | 336 |
| abstract_inverted_index.Methods | 74 |
| abstract_inverted_index.Results | 185 |
| abstract_inverted_index.Science | 334 |
| abstract_inverted_index.Support | 330 |
| abstract_inverted_index.address | 94 |
| abstract_inverted_index.between | 204, 284 |
| abstract_inverted_index.capable | 41 |
| abstract_inverted_index.cardiac | 45, 293 |
| abstract_inverted_index.central | 3, 71, 105, 136, 193, 267 |
| abstract_inverted_index.effort, | 144, 152 |
| abstract_inverted_index.failure | 28 |
| abstract_inverted_index.further | 311 |
| abstract_inverted_index.machine | 59 |
| abstract_inverted_index.monitor | 40, 69, 88, 207, 273 |
| abstract_inverted_index.records | 101, 121, 203 |
| abstract_inverted_index.related | 323 |
| abstract_inverted_index.results | 309 |
| abstract_inverted_index.scores. | 184 |
| abstract_inverted_index.scoring | 96 |
| abstract_inverted_index.snoring | 146 |
| abstract_inverted_index.squared | 220 |
| abstract_inverted_index.testing | 11 |
| abstract_inverted_index.(89/90), | 249 |
| abstract_inverted_index.(N=388). | 138 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.CAI≥10 | 242 |
| abstract_inverted_index.Medical, | 34, 340 |
| abstract_inverted_index.National | 333 |
| abstract_inverted_index.Research | 337 |
| abstract_inverted_index.analyzed | 77 |
| abstract_inverted_index.classify | 64, 148 |
| abstract_inverted_index.compared | 180, 250 |
| abstract_inverted_index.estimate | 173 |
| abstract_inverted_index.evaluate | 164 |
| abstract_inverted_index.identify | 296 |
| abstract_inverted_index.learning | 60 |
| abstract_inverted_index.motivate | 310 |
| abstract_inverted_index.outcomes | 322 |
| abstract_inverted_index.patients | 299 |
| abstract_inverted_index.subjects | 80, 178 |
| abstract_inverted_index.therapy, | 302 |
| abstract_inverted_index.Algorithm | 186 |
| abstract_inverted_index.Alliance; | 338 |
| abstract_inverted_index.CAI≥15. | 258 |
| abstract_inverted_index.agreement | 281 |
| abstract_inverted_index.algorithm | 262 |
| abstract_inverted_index.alongside | 292 |
| abstract_inverted_index.breathing | 20 |
| abstract_inverted_index.collected | 269 |
| abstract_inverted_index.comparing | 231 |
| abstract_inverted_index.detecting | 43 |
| abstract_inverted_index.detection | 291 |
| abstract_inverted_index.developed | 57 |
| abstract_inverted_index.duration, | 153 |
| abstract_inverted_index.expedited | 301 |
| abstract_inverted_index.favorable | 275 |
| abstract_inverted_index.features. | 156 |
| abstract_inverted_index.five-fold | 158 |
| abstract_inverted_index.follow-up | 306 |
| abstract_inverted_index.function. | 49 |
| abstract_inverted_index.important | 13 |
| abstract_inverted_index.including | 26 |
| abstract_inverted_index.oximetry, | 142 |
| abstract_inverted_index.referral, | 304 |
| abstract_inverted_index.suspected | 82 |
| abstract_inverted_index.treatment | 17 |
| abstract_inverted_index.Conclusion | 259 |
| abstract_inverted_index.Home-based | 289 |
| abstract_inverted_index.cardiology | 303 |
| abstract_inverted_index.chest-worn | 39, 272 |
| abstract_inverted_index.classifier | 130 |
| abstract_inverted_index.comparable | 279 |
| abstract_inverted_index.evaluation | 327 |
| abstract_inverted_index.monitoring | 294 |
| abstract_inverted_index.overnight. | 92 |
| abstract_inverted_index.reference, | 55 |
| abstract_inverted_index.underlying | 23 |
| abstract_inverted_index.validation | 160, 312 |
| abstract_inverted_index.Foundation; | 335 |
| abstract_inverted_index.Identifying | 2 |
| abstract_inverted_index.Sensitivity | 237 |
| abstract_inverted_index.annotations | 118 |
| abstract_inverted_index.appropriate | 16 |
| abstract_inverted_index.coefficient | 214 |
| abstract_inverted_index.conditions, | 25 |
| abstract_inverted_index.correlation | 213 |
| abstract_inverted_index.event-level | 165, 187, 276 |
| abstract_inverted_index.events/hour | 230, 243 |
| abstract_inverted_index.management. | 329 |
| abstract_inverted_index.ninety-five | 79 |
| abstract_inverted_index.obstructive | 133, 265 |
| abstract_inverted_index.performance | 277 |
| abstract_inverted_index.respiratory | 65, 143 |
| abstract_inverted_index.sensitivity | 188 |
| abstract_inverted_index.specificity | 190, 239 |
| abstract_inverted_index.Introduction | 1 |
| abstract_inverted_index.arrhythmias, | 46 |
| abstract_inverted_index.demonstrated | 274 |
| abstract_inverted_index.events/hour, | 225 |
| abstract_inverted_index.laboratories | 110 |
| abstract_inverted_index.obstructive. | 73 |
| abstract_inverted_index.performance. | 166 |
| abstract_inverted_index.twenty-eight | 120 |
| abstract_inverted_index.Subject-level | 199 |
| abstract_inverted_index.differentiate | 132, 264 |
| abstract_inverted_index.fibrillation. | 31 |
| abstract_inverted_index.heterogeneity | 97 |
| abstract_inverted_index.investigation | 320 |
| abstract_inverted_index.laboratories. | 288 |
| abstract_inverted_index.subject-level | 174 |
| abstract_inverted_index.cardiovascular | 24 |
| abstract_inverted_index.simultaneously | 89 |
| abstract_inverted_index.polysomnography | 51 |
| abstract_inverted_index.multi-diagnostic | 38 |
| abstract_inverted_index.sleep-disordered | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.23206693 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |