Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep
Context: Accurate discrimination between obstructive and central hypopneas requires quantitative assessments of respiratory effort by esophageal pressure (OeP) measurements, which preclude widespread implementation in sleep medicine practice. Mandibular Movement (MM) signals are closely associated w...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2019.00828/full |
| _version_ | 1818249200050110464 |
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| author | Jean-Benoit Martinot Jean-Benoit Martinot Nhat-Nam Le-Dong Valerie Cuthbert Stephane Denison Jean C. Borel David Gozal Jean L. Pépin |
| author_facet | Jean-Benoit Martinot Jean-Benoit Martinot Nhat-Nam Le-Dong Valerie Cuthbert Stephane Denison Jean C. Borel David Gozal Jean L. Pépin |
| author_sort | Jean-Benoit Martinot |
| collection | DOAJ |
| description | Context: Accurate discrimination between obstructive and central hypopneas requires quantitative assessments of respiratory effort by esophageal pressure (OeP) measurements, which preclude widespread implementation in sleep medicine practice. Mandibular Movement (MM) signals are closely associated with diaphragmatic effort during sleep.Objective: We aimed at reliably detecting obstructive off central hypopneas events using MM statistical characteristics.Methods: A bio-signal learning approach was implemented whereby raw MM fragments corresponding to normal breathing (NPB; n = 501), central (n = 263), and obstructive hypopneas (n = 1861) were collected from 28 consecutive patients (mean age = 54 years, mean AHI = 34.7 n/h) undergoing in-lab polysomnography (PSG) coupled with a MM magnetometer, and OeP recordings. Twenty three input features were extracted from raw data fragments to explore distinctive changes in MM signals. A Random Forest model was built upon those input features to classify the central and obstructive hypopnea events. External validation and interpretive analysis were performed to evaluate the model's performance and the contribution of each feature to the model's output.Results: Obstructive hypopneas were characterized by a longer duration (21.9 vs. 17.8 s, p < 10−6), more extreme low values (p < 10−6), a more negative trend reflecting mouth opening amplitude, wider variation, and the asymmetrical distribution of MM amplitude. External validation showed a reliable performance of the MM features-based classification rule (Kappa coefficient = 0.879 and a balanced accuracy of 0.872). The interpretive analysis revealed that event duration, lower percentiles, central tendency, and the trend of MM amplitude were the most important determinants of events.Conclusions: MM signals can be used as surrogate markers of OeP to differentiate obstructive from central hypopneas during sleep. |
| first_indexed | 2024-12-12T15:32:42Z |
| format | Article |
| id | doaj.art-d968233b2dda4958abd00381bdadb4c6 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-12T15:32:42Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-d968233b2dda4958abd00381bdadb4c62022-12-22T00:20:05ZengFrontiers Media S.A.Frontiers in Neurology1664-22952019-08-011010.3389/fneur.2019.00828476587Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During SleepJean-Benoit Martinot0Jean-Benoit Martinot1Nhat-Nam Le-Dong2Valerie Cuthbert3Stephane Denison4Jean C. Borel5David Gozal6Jean L. Pépin7Sleep Laboratory, CHU UCL Namur Site Sainte-Elisabeth, Namur, BelgiumInstitute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, BelgiumResearch and Development Center, Respisom, Erpent, BelgiumResearch and Development Center, Respisom, Erpent, BelgiumResearch and Development Center, Respisom, Erpent, BelgiumHP2 INSERM U1042, Université Grenoble Alpes, Grenoble, FranceDepartment of Child Health and Child Health Research Institute, University of Missouri, Columbia, MO, United StatesHP2 INSERM U1042, Université Grenoble Alpes, Grenoble, FranceContext: Accurate discrimination between obstructive and central hypopneas requires quantitative assessments of respiratory effort by esophageal pressure (OeP) measurements, which preclude widespread implementation in sleep medicine practice. Mandibular Movement (MM) signals are closely associated with diaphragmatic effort during sleep.Objective: We aimed at reliably detecting obstructive off central hypopneas events using MM statistical characteristics.Methods: A bio-signal learning approach was implemented whereby raw MM fragments corresponding to normal breathing (NPB; n = 501), central (n = 263), and obstructive hypopneas (n = 1861) were collected from 28 consecutive patients (mean age = 54 years, mean AHI = 34.7 n/h) undergoing in-lab polysomnography (PSG) coupled with a MM magnetometer, and OeP recordings. Twenty three input features were extracted from raw data fragments to explore distinctive changes in MM signals. A Random Forest model was built upon those input features to classify the central and obstructive hypopnea events. External validation and interpretive analysis were performed to evaluate the model's performance and the contribution of each feature to the model's output.Results: Obstructive hypopneas were characterized by a longer duration (21.9 vs. 17.8 s, p < 10−6), more extreme low values (p < 10−6), a more negative trend reflecting mouth opening amplitude, wider variation, and the asymmetrical distribution of MM amplitude. External validation showed a reliable performance of the MM features-based classification rule (Kappa coefficient = 0.879 and a balanced accuracy of 0.872). The interpretive analysis revealed that event duration, lower percentiles, central tendency, and the trend of MM amplitude were the most important determinants of events.Conclusions: MM signals can be used as surrogate markers of OeP to differentiate obstructive from central hypopneas during sleep.https://www.frontiersin.org/article/10.3389/fneur.2019.00828/fullsleep apnea syndromehypopnearespiratory effortmandibular movementsobstructive hypopneacentral hypopnea |
| spellingShingle | Jean-Benoit Martinot Jean-Benoit Martinot Nhat-Nam Le-Dong Valerie Cuthbert Stephane Denison Jean C. Borel David Gozal Jean L. Pépin Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep Frontiers in Neurology sleep apnea syndrome hypopnea respiratory effort mandibular movements obstructive hypopnea central hypopnea |
| title | Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep |
| title_full | Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep |
| title_fullStr | Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep |
| title_full_unstemmed | Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep |
| title_short | Respiratory Mandibular Movement Signals Reliably Identify Obstructive Hypopnea Events During Sleep |
| title_sort | respiratory mandibular movement signals reliably identify obstructive hypopnea events during sleep |
| topic | sleep apnea syndrome hypopnea respiratory effort mandibular movements obstructive hypopnea central hypopnea |
| url | https://www.frontiersin.org/article/10.3389/fneur.2019.00828/full |
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