###### 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. non-invasive ventilation; lung diseases; work of breathing; respiratory muscles; surface electromyography; machine learning +
Footnote †: journal: Medical  
hyperinflation, airflow obstruction (Chronic Obstructive Pulmonary Disease or COPD patients) or obese people [4; 7; 8]. In addition, WOB values in the range of 1.3-1.8 J/L are considered elevated, making it impossible to maintain an adequate spontaneous ventilation for a long period of time [5; 8]. According to the state of the art, the use of an esophageal balloon catheter is required to accurately measure the work of breathing. However, this technique is invasive and uncomfortable for the patient, which restricts its use in clinical practice [9; 10]. Several clinical strategies to detect an increase in work of breathing include distress or dyspnea, vital signs, physical signs of increased breathing effort, breathing rate, tidal volume, and frequency/tidal volume ratio [3; 5; 11]. However, these strategies are not reliable in all cases, which hampers its generalization [3]. Dyspnea is defined as \"breathing discomfort\", which occurs when there is an imbalance between the load imposed upon the respiratory muscles and the capacity of these to overcome it. For that reason, many authors have successfully used surface electromyography (sEMG) signals of respiratory muscles to quantify the level of dyspnea [12; 13; 14]. Therefore, a relationship between respiratory muscle activity and work of breathing can be inferred. Some authors have proposed indexes from sEMG signals of respiratory muscles, in the following domains: time, frequency, and time-frequency [12; 15; 16; 17]. However, the relationship between respiratory muscle activity and work of breathing has not been proven yet, because those authors had no WOB measurements. Furthermore, considering that multiple respiratory muscles are involved, patients may have different ventilation patterns to achieve the same work, and to our knowledge this has not been studied. Some researchers have studied muscle activity patterns from sEMG signals and used different machine learning algorithms to recognize activation patterns in several muscles, mainly of the limbs. [PERSON] et al. [18] presented a review describing the use of sEMG, showing selected features, used muscles, classification algorithms, objective, and the accuracy of the studies. A common objective was the classification of patients with neuromuscular diseases and muscular fatigue using sEMG signals of the Biceps brachii muscle. Consequently, classifiers like the Simple Fisher Linear Discriminant Analysis (LDA), statistical K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks (NN), among others, were used.