Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics
Early assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient c...
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2025-01-01
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| author | Ghada Ben Othman Amani R. Ynineb Erhan Yumuk Hamed Farbakhsh Cristina Muresan Isabela Roxana Birs Alexandra De Raeve Cosmin Copot Clara M. Ionescu Dana Copot |
| author_facet | Ghada Ben Othman Amani R. Ynineb Erhan Yumuk Hamed Farbakhsh Cristina Muresan Isabela Roxana Birs Alexandra De Raeve Cosmin Copot Clara M. Ionescu Dana Copot |
| author_sort | Ghada Ben Othman |
| collection | DOAJ |
| description | Early assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient cooperation, which may not be available in fragile individuals. The Forced Oscillation Technique (FOT) is a non-invasive measurement method, only based on the tidal breathing at rest from the patient for a limited time period. The proposed solution integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors to enhance respiratory mechanics information with heart rate variability. Data were collected over a two-hour period from six healthy volunteers, measuring respiratory impedance every 7 min and continuously recording physiological parameters. The best-fitting fractional-order models for impedance data were identified using genetic algorithms. This study also explores the correlation between impedance model parameters and EQV data, discussing the potential of AI tools for forecasting respiratory properties. Our findings indicate that combined monitoring techniques and AI analysis provides additional complementary information, subsequently aiding the improved evaluation of respiratory function and tissue mechanics. The proposed protocol allows for ambulatory assessment and can be easily performed in normal breathing conditions. |
| format | Article |
| id | doaj-art-177ad693ce054d4fbf9a31c2a1ab64d7 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-177ad693ce054d4fbf9a31c2a1ab64d72025-01-24T13:20:41ZengMDPI AGApplied Sciences2076-34172025-01-0115275110.3390/app15020751Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory MechanicsGhada Ben Othman0Amani R. Ynineb1Erhan Yumuk2Hamed Farbakhsh3Cristina Muresan4Isabela Roxana Birs5Alexandra De Raeve6Cosmin Copot7Clara M. Ionescu8Dana Copot9Department of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumDepartment of Automation, Technical University Cluj-Napoca, Memorandumului Street 20, 400114 Cluj, RomaniaDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumFashion, Textiles and Innovation Lab (FTILab+), HOGENT University of Applied Science and Arts, Buchtenstraat 11, 9051 Ghent, BelgiumFashion, Textiles and Innovation Lab (FTILab+), HOGENT University of Applied Science and Arts, Buchtenstraat 11, 9051 Ghent, BelgiumDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumDepartment of Electromechanics, System and Metal Engineering, Ghent University, 9052 Ghent, BelgiumEarly assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient cooperation, which may not be available in fragile individuals. The Forced Oscillation Technique (FOT) is a non-invasive measurement method, only based on the tidal breathing at rest from the patient for a limited time period. The proposed solution integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors to enhance respiratory mechanics information with heart rate variability. Data were collected over a two-hour period from six healthy volunteers, measuring respiratory impedance every 7 min and continuously recording physiological parameters. The best-fitting fractional-order models for impedance data were identified using genetic algorithms. This study also explores the correlation between impedance model parameters and EQV data, discussing the potential of AI tools for forecasting respiratory properties. Our findings indicate that combined monitoring techniques and AI analysis provides additional complementary information, subsequently aiding the improved evaluation of respiratory function and tissue mechanics. The proposed protocol allows for ambulatory assessment and can be easily performed in normal breathing conditions.https://www.mdpi.com/2076-3417/15/2/751fractional-order impedance modellow-frequency oscillation techniquevital signal monitoringrespiratory mechanicsartificial intelligencecontinuous monitoring |
| spellingShingle | Ghada Ben Othman Amani R. Ynineb Erhan Yumuk Hamed Farbakhsh Cristina Muresan Isabela Roxana Birs Alexandra De Raeve Cosmin Copot Clara M. Ionescu Dana Copot Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics Applied Sciences fractional-order impedance model low-frequency oscillation technique vital signal monitoring respiratory mechanics artificial intelligence continuous monitoring |
| title | Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics |
| title_full | Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics |
| title_fullStr | Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics |
| title_full_unstemmed | Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics |
| title_short | Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics |
| title_sort | integrated solution combining low frequency forced oscillation technique and continuous equivital sensor monitoring for assessment of non invasive ambulatory respiratory mechanics |
| topic | fractional-order impedance model low-frequency oscillation technique vital signal monitoring respiratory mechanics artificial intelligence continuous monitoring |
| url | https://www.mdpi.com/2076-3417/15/2/751 |
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