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| Name | Class |
|---|---|
| SRETT | UNKNOWN |
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Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory disease caused by smoking and associated with significant morbidity due to recurrent exacerbations or even hospitalizations. It is one of the leading causes of death in rich countries.
Early detection of exacerbations of the disease is thus an economic and public health issue in the world. Finding tools for automatic and early detection of an exacerbation is the subject of the study. A continuous analysis of the frequency and shape of the patients' breathing will be used.
As a first step in this study, investigators want to measure the frequency and shape of breathing in healthy subjects as they execute standard activities. These activities include resting, drinking, coughing, speaking, breathing through the mouth, physical exercise and recuperation.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy subjects | Experimental | Subjects not presenting any pulmonary pathology that will follow the intervention Oxygen therapy simulation. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Oxygen therapy simulation | Other | The intervention consists of reproducing oxygen therapy situations in healthy individuals by replacing oxygen bottles with bottles of air. A TeleOx® device will be placed on the circuit between the source and the nasal cannula for respiratory signals recording. The intervention will last 30 minutes, containing the following activities in the given order:
Recordings will be retrieved after each test. |
| Measure | Description | Time Frame |
|---|---|---|
| Pressure | The monitoring device used records the nasal pressure from users at 10 Hz (one measure every 0.1 seconds). | 10 Hz for 30 minutes |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Jesus Gonzalez-Bermejo | Groupe Hospitalier Pitie-Salpetriere | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| INSERM-UPC UMR_S 1158, Service de Pneumologie et Réanimation, GH Pitié-Salpêtrière | Paris | 75013 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34275469 | Derived | Pegoraro JA, Lavault S, Wattiez N, Similowski T, Gonzalez-Bermejo J, Birmele E. Machine-learning based feature selection for a non-invasive breathing change detection. BioData Min. 2021 Jul 18;14(1):33. doi: 10.1186/s13040-021-00265-8. |
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