## 4 Discussion
In future work, the classifiers should be validated to increase the accuracy, recording the real WOB with invasive methods in patients with invasive mechanical ventilation. The proposed algorithm might be useful in critical care medicine, due to its ability to identify the level of work of breathing in a non-invasive manner. With a specificity of 83.67% and a sensitivity (Group 2 and Group 3) of 96.36% (see Figure 5 and Table 6), it is possible to predict who could have a successful weaning, which in terms of low WOB is in line with the study of [PERSON] et al. [8], where 96% of patients with a work of breathing lower than 0.8 J/L had a successful weaning process. The model proposed in this study relates work of breathing with the maximum amplitudes of sEMG signals of the diaphragm and two accessory muscles, the intercostal and sternocleidomastoid. However, due to the normalization process, it was necessary to find different coefficients according to the level of WOB. The coefficients indicate the contribution to the ventilatory process of each of the muscles. In all cases, the muscle with the most contribution was the diaphragm with 55% for the first group and 53% for the other groups; additionally, the remaining contribution was distributed between the other respiratory muscles. The coefficients (\(\alpha\)1 y \(\alpha\)2) in both experiments were similar, because the minimum error for all the dataset was in the median value of the group. In that case, it was possible to consider only one model for both cases, restrictive and obstructive patterns. The proposed approach estimates the value of work of breathing with an accuracy of 80.43%. Other authors have shown non-invasive techniques to estimate the work of breathing. [PERSON] et al. [41] presented a model that estimates the work of breathing of mechanically ventilated patients, using ventilatory signals such as airway pressure, volume, and airflow. Their model offers favorable results in cases in which the patients have normal and controlled respiratory efforts, but it has limitations for uncontrolled efforts. [PERSON] et al. [11; 20] presented an Artificial Neuronal Network to estimate the power of breathing or work of breathing per minute from parameters like spontaneous minute ventilation, inspiratory trigger pressure, inspiratory flow rise time, intrinsic PEEP and respiratory muscle pressure, where the last two parameters depend on the respiratory system compliance and airway resistance calculated from controlled ventilations. The model presented in this study can predict the WOB at different levels during total spontaneous ventilation and has the advantage that it does not need ventilatory signals, i.e., it is possible to know the work of breathing in situations that do not require mechanical ventilation or devices to record ventilatory signals that can affect the respiratory mechanics. This pilot study suggests that the work of breathing can be estimated by measuring the activity of respiratory muscles using surface electromyography. In accordance with other authors who inferred it in previous works, like [PERSON], et al. [15], who found a promising index to assess the participation of respiratory muscle on spontaneous breathing test in poisoned patients, [PERSON], et al. [14], who showed that measurements of accessory inspiratory muscles by sEMG can contribute to a reliable non-invasive indicator of respiratory effort in mechanically ventilated patients, and [PERSON] et al. [21], who showed that changes in indexes from sEMG of the diaphragm and sternocleidomastoid are related to changes in the level of PEEP. Furthermore, the work of breathing was estimated in different ventilatory patterns in spontaneous ventilation, which corroborates the close relationship, not only of the diaphragm, but of the accessory muscles with the ventilatory process.