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Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data.

Hongle Wu1, Takafumi Kato2, Tomomi Yamada3

  • 1Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Japan.

Artificial Intelligence in Medicine
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Summary
This summary is machine-generated.

This study introduces a novel method to analyze sleep patterns by clustering overnight sound events. The findings show sound data can reveal sleep stages and individual sleep quality, offering new sleep monitoring insights.

Keywords:
Pairwise F-measurePolysomnographySelf-organizing mapSleep patternSleep stageSound data

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Area of Science:

  • Computational auditory processing
  • Sleep science and chronobiology
  • Machine learning applications

Background:

  • Sleep pattern analysis traditionally relies on polysomnography or actigraphy.
  • Existing methods may not fully capture the nuances of sleep disturbances or stages.
  • Acoustic monitoring offers a non-invasive, continuous data stream for sleep assessment.

Purpose of the Study:

  • To develop and validate a novel method for discovering sleep patterns using clustered sound events.
  • To enhance the self-organizing map algorithm for fine-grained visualization of sleep-related acoustic phenomena.
  • To establish a correlation between specific sound events and sleep stages for improved sleep monitoring.

Main Methods:

  • Kernelized and sequence-based extensions of the self-organizing map algorithm were employed.
  • Features from established sound processing techniques and popular kernel functions were utilized.
  • Clustering algorithms were applied to categorize and analyze recorded sleep sound events.

Main Results:

  • The proposed method successfully visualized the distribution and temporal changes of sleep-related sound events.
  • A direct correlation was demonstrated between acoustic event patterns and individual sleep patterns.
  • Visualization of cluster dynamics revealed relationships between sound events and distinct sleep stages.

Conclusions:

  • Sound event clustering provides a novel and effective approach to sleep pattern discovery.
  • The method offers a new perspective for non-invasive sleep monitoring and quality assessment.
  • Further research is warranted to explore the full potential of acoustic data in sleep studies.