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Estimation of Work of Breathing from Respiratory Muscle Activity In Spontaneous Ventilation: A Pilot Study

1
Bioinstrumentation and Clinical Engineering Research Group—GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín 050010, Colombia
2
Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB) and Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine, CIBER-BBN, Universitat Politècnica de Catalunya, 08928 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2007; https://doi.org/10.3390/app9102007
Received: 22 March 2019 / Revised: 18 April 2019 / Accepted: 9 May 2019 / Published: 16 May 2019
(This article belongs to the Special Issue Signals in Health Care)
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Abstract

Work of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use of qualitative observations such as the level of dyspnea is preferred as a possible indicator of WOB level. In this pilot study, the activity of three respiratory muscles were recorded on healthy subjects through surface electromyography while they were under non-invasive mechanical ventilation, using restrictive and obstructive maneuvers to obtain different WOB levels. The respiratory pattern between restrictive and obstructive maneuvers was classified with the Nearest Neighbor Algorithm with a 91% accuracy and a neural network model helped classify the samples into three WOB levels with a 89% accuracy, Low: [0.3–0.8) J/L, Medium: [0.8–1.3] J/L and Elevated: (1.3–1.8] J/L, demonstrating the relationship between the respiratory muscle activity and WOB. This technique is a promising tool for the healthcare staff in the decision-making process when selecting the best ventilation settings to maintain a low WOB. This study identified a model to estimate the WOB in different ventilatory patterns, being an alternative to invasive conventional techniques. View Full-Text
Keywords: non-invasive ventilation; lung diseases; work of breathing; respiratory muscles; surface electromyography; machine learning non-invasive ventilation; lung diseases; work of breathing; respiratory muscles; surface electromyography; machine learning
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MDPI and ACS Style

Muñoz, I.C.; Hernández, A.M.; Mañanas, M.Á. Estimation of Work of Breathing from Respiratory Muscle Activity In Spontaneous Ventilation: A Pilot Study. Appl. Sci. 2019, 9, 2007.

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