Automatic Segmentation to Cluster Patterns of Breathing in Sleep Apnea

Villads Hulgaard Joergensen, Umaer Hanif, Poul Jennum, Emmanuel Mignot, Asbjørn W. Helge, Helge Bjarup Dissing Sørensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Annotation of polysomnography (PSG) recordings for diagnosis of obstructive sleep apnea (OSA) is a standard procedure but an expensive and time-consuming process for clinicians. To aid clinicians in this process we present a data driven unsupervised hierarchical clustering approach for detection and visual presentation of breathing patterns in PSG recordings. The aim was to develop a model independent of manual annotations to detect and visualize respiratory events related to OSA. 10 recordings from the Sleep Heart Health Study database were used, and the proposed algorithm was evaluated based on the manually annotated events for each recording. The algorithm reached an F1-score of 0.58 across the 10 recordings when detecting the presence of an event vs. no event and a 100% correct diagnosis prediction of OSA when predicting if apnea-hypopnea index (AHI) ≥ 15, which is a clinically meaningful cut-off. The F1-score may be due to imprecise placement of events, difficulty distinguishing between hypopneas and stable breathing, and variations in scoring. In conclusion the performance can be improved despite the strong agreement in diagnostics. The method is a proof of concept that a clustering method can detect and visualize breathing patterns related to OSA while maintaining a correct diagnosis.
Original languageEnglish
Title of host publicationProceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherIEEE
Publication date2021
Pages164-168
ISBN (Print)978-1-7281-1180-3
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual event
Duration: 1 Nov 20215 Nov 2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationVirtual event
Period01/11/202105/11/2021
SeriesAnnual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
ISSN2694-0604

Keywords

  • Humans
  • Polysomnography
  • Respiration
  • Sleep
  • Sleep Apnea Syndromes
  • Sleep Apnea, Obstructive

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