The aim of this pilot study was to evaluate the feasibility of surface electromyographic signal derived indexes for the prediction of weaning outcomes among mechanically ventilated subjects after cardiac surgery. A sample of 10 postsurgical adult subjects who received cardiovascular surgery that did not meet the criteria for early extubation were included. Surface electromyographic signals from diaphragm and ventilatory variables were recorded during the weaning process, with the moment determined by the medical staff according to their expertise. Several indexes of respiratory muscle expenditure from surface electromyography using linear and non-linear processing techniques were evaluated. Two groups were compared: successfully and unsuccessfully weaned patients. The obtained indexes allow estimation of the diaphragm activity of each subject, showing a correlation between high expenditure and weaning test failure. Surface electromyography is becoming a promising procedure for assessing the state of mechanically ventilated patients, even in complex situations such as those that involve a patient after cardiovascular surgery.

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.

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter validation processes. This paper shows a statistical and a Delphi validation for two modules of a web application used for respiratory system learning: (I) “Anatomy and Physiology” and (II) “Work of Breathing Indexes”. For statistical validation, population and individual analyses were made using a database of healthy men to compare experimental and model-predicted data. For both modules, the predicted values followed the trend marked by the experimental data in the population analysis, while in the individual analysis, the predicted errors were 9.54% and 25.38% for maximal tidal volume and airflow, respectively, and 6.55%, 9.33%, and 11.77% for rapid shallow breathing index, work of breathing, and maximal inspiratory pressure, respectively. For the Delphi validation, an average higher than 4 was obtained after health professionals evaluated both modules from 1 to 5. In conclusion, both modules are good tools for respiratory system learning processes. The studied parameters behaved consistently with the expressions that describe ventilatory dynamics and were correlated with experimental data; furthermore, they had great acceptance by specialists.

Heart and respiratory rate monitoring during sleep enables the detection of physiological irregularities through contact or contactless methods. Traditional approaches like polysomnography are accurate but costly, ergonomically limited, and often poorly accepted by patients. Smart Bedding^® is a novel, flexible bedsheet equipped with a high-resolution sensor network that records movement, pressure, sound, temperature, and humidity throughout the night. This study aimed to estimate cardiorespiratory parameters using the Smart Bedding^® IMU. Data from 30 participants sleeping on Smart Bedding^® while undergoing simultaneous polysomnography were analyzed. A robust and low-cost preprocessing pipeline was developed; estimation was performed using zero-crossing, peak detection, and Burg’s method for comparison, and validation was conducted using polysomnography as the gold-standard reference. Respiratory and heart rates were accurately estimated, achieving overall accuracies of 93.9% and 88.7% using zero-crossing and peak detection, respectively. Respiratory rate estimation showed no significant limitations across the frequency spectrum or among sleeping positions. However, heart rate estimation accuracy decreased when the frequency was below 55 BPM or when participants slept in a lateral sleep position, likely due to reduced cardiac signal power. Overall, the proposed methodology accurately tracked respiratory and cardiac patterns throughout the night, supporting Smart Bedding^® as a promising tool for future sleep tracking applications.