Automatic detection of AutoPEEP during controlled mechanical ventilation
| العنوان: | Automatic detection of AutoPEEP during controlled mechanical ventilation |
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| المؤلفون: | Quang Thang Nguyen, Erwan L'Her, Dominique Pastor |
| المساهمون: | Lab-STICC_TB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Optimisation continue des actions thérapeutiques par l'intégration d'informations, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM), Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque, BMC, Ed. |
| المصدر: | BioMedical Engineering BioMedical Engineering OnLine, BioMed Central, 2012 BioMedical Engineering OnLine, BioMed Central, 2012, 11 (1), pp.32. ⟨10.1186/1475-925X-11-32⟩ BioMedical Engineering OnLine, 2012, 11 (1), pp.32. ⟨10.1186/1475-925X-11-32⟩ BioMedical Engineering OnLine, Vol 11, Iss 1, p 32 (2012) |
| بيانات النشر: | Springer Nature |
| مصطلحات موضوعية: | Flow waveform, Sequential decision, lcsh:Medical technology, [SDV.MHEP.CHI] Life Sciences [q-bio]/Human health and pathology/Surgery, Interface (computing), Wavelet Analysis, Biomedical Engineering, [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, 01 natural sciences, Signal, Biomaterials, Automation, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Wavelet, Pressure, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), Signal norm testing, 0101 mathematics, Simulation, Patient-ventilator interaction, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Models, Statistical, Radiological and Ultrasound Technology, business.industry, Dynamic hyperinflation detection, Research, Continuous monitoring, Detector, 030208 emergency & critical care medicine, General Medicine, Respiration, Artificial, AutoPEEP detection, lcsh:R855-855.5, Exhalation, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms |
| الوصف: | Background Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure) with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow does not return to zero before the next inspiration, AutoPEEP is present. This simple detection however requires the eye of an expert clinician at the patient’s bedside. An automatic detection of AutoPEEP should be helpful to optimize care. Methods In this paper, a platform for automatic detection of AutoPEEP based on the flow signal available on most of recent mechanical ventilators is introduced. The detection algorithms are developed on the basis of robust non-parametric hypothesis testings that require no prior information on the signal distribution. In particular, two detectors are proposed: one is based on SNT (Signal Norm Testing) and the other is an extension of SNT in the sequential framework. The performance assessment was carried out on a respiratory system analog and ex-vivo on various retrospectively acquired patient curves. Results The experiment results have shown that the proposed algorithm provides relevant AutoPEEP detection on both simulated and real data. The analysis of clinical data has shown that the proposed detectors can be used to automatically detect AutoPEEP with an accuracy of 93% and a recall (sensitivity) of 90%. Conclusions The proposed platform provides an automatic early detection of AutoPEEP. Such functionality can be integrated in the currently used mechanical ventilator for continuous monitoring of the patient-ventilator interface and, therefore, alleviate the clinician task. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1475-925X |
| DOI: | 10.1186/1475-925x-11-32 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::128252766efa93845dcddf34c54f5f09Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....128252766efa93845dcddf34c54f5f09 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1475925X |
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| DOI: | 10.1186/1475-925x-11-32 |