Time-frequency ridge analysis of sleep stage transitions

C. McCausland, P. Biglarbeigi, R. Bond, G. Yadollahikhales, D. Finlay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.

Original languageEnglish
Title of host publication2022 IEEE Signal Processing in Medicine and Biology Symposium
Subtitle of host publicationProceedings
Place of PublicationPhiladelphia, PA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781665470292
ISBN (Print)9781665470308
Publication statusPublished - 3 Dec 2022
Event2022 IEEE Signal Processing in Medicine and Biology Symposium - Temple University, Philadelphia, United States
Duration: 3 Dec 20223 Dec 2022

Publication series

Name2022 IEEE Signal Processing in Medicine and Biology Symposium Proceedings
ISSN (Print)2372-7241
ISSN (Electronic)2473-716X


Conference2022 IEEE Signal Processing in Medicine and Biology Symposium
Abbreviated titleSPMB 2022
Country/TerritoryUnited States
Internet address


  • Observers
  • Signal processing
  • Time-frequency analysis
  • Automation
  • Sleep
  • Signal processing algorithms
  • Manuals

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Medicine (miscellaneous)


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