Tracking Breathing During Sleep With Non-contact Sensors
| Status: | Recruiting |
|---|---|
| Conditions: | Insomnia Sleep Studies, Pulmonary |
| Therapuetic Areas: | Psychiatry / Psychology, Pulmonary / Respiratory Diseases |
| Healthy: | No |
| Age Range: | 21 - 89 |
| Updated: | 4/21/2016 |
| Start Date: | October 2012 |
| End Date: | August 2016 |
| Contact: | Brian R Snider |
| Email: | sniderb@ohsu.edu |
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.
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.
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.
Inclusion Criteria:
- Age 21-89
- No self-reported sleep breathing problems
Exclusion Criteria:
- Positive diagnosis for sleep breathing problem (e.g., obstructive sleep apnea)
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