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Tracking Breathing During Sleep With Non-contact Sensors

This study is currently recruiting participants. (see Contacts and Locations)
Verified October 2014 by Oregon Health and Science University
Sponsor:
Information provided by (Responsible Party):
Alexander Kain, Oregon Health and Science University
ClinicalTrials.gov Identifier:
NCT01680380
First received: September 4, 2012
Last updated: October 16, 2014
Last verified: October 2014

September 4, 2012
October 16, 2014
October 2012
August 2015   (final data collection date for primary outcome measure)
Breathing sounds are evident in overnight audio recordings [ Time Frame: Night of recording ] [ Designated as safety issue: No ]
This study aims to track breathing during sleep using a high-quality audio interface. Our primary objective is to determine if quiet breathing sounds are visible (in the spectral domain) to trained human labelers.
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Complete list of historical versions of study NCT01680380 on ClinicalTrials.gov Archive Site
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Tracking Breathing During Sleep With Non-contact Sensors
Tracking Breathing During Sleep With Non-contact Sensors

The purpose of this study is to evaluate the feasibility of tracking breathing during sleep with non-contact sensors (for example, microphones or wireless movement sensors). The investigators will use the data collected with these sensors to develop algorithms for tracking breathing during sleep. The investigators will assess the performance of the algorithms by comparing automatic output against manually-generated labels.

Subjects will be asked to place non-contact sensors (for example, ambient microphones, wireless movement sensors) in their home sleep environment. No sensors will be attached to or otherwise in contact with the subject's body. The subjects will start the data collection before they fall asleep, and stop the data collection the next morning when they wake. The subjects will then return the sensors to the investigator for analysis.

The investigators will study the data and associated manual labeling. The investigators will develop algorithms that use statistical and machine-learning methods to train computer models designed to track breathing automatically. The investigators will compare the automatic output against manually generated labels to determine breath-tracking accuracy.

Observational
Observational Model: Case-Only
Time Perspective: Cross-Sectional
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Non-Probability Sample

Participants will be recruited from the PI's department (OHSU faculty, Ph.D. students) by email and the investigators' personal acquaintances. Participants must be age 21-100 and have no self-reported sleep breathing problems.

  • Sleep Apnea Syndromes
  • Snoring
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At-Home
Overnight sleep at home
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*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruiting
20
August 2015
August 2015   (final data collection date for primary outcome measure)

Inclusion Criteria:

  • Age 21-100
  • No self-reported sleep breathing problems

Exclusion Criteria:

  • Positive diagnosis for sleep breathing problem (e.g., obstructive sleep apnea)
Both
21 Years and older
Yes
Contact: Brian R Snider sniderb@ohsu.edu
United States
 
NCT01680380
IRB00008533
No
Alexander Kain, Oregon Health and Science University
Oregon Health and Science University
Not Provided
Principal Investigator: Alexander Kain, Ph.D. Oregon Health and Science University
Oregon Health and Science University
October 2014

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP