Publications

Surface electromyographic (sEMG) signals from the diaphragm has become a valuable tool for monitoring muscle activity during the weaning process from mechanical ventilation. However, EMG signals are inherently nonlinear and susceptible to noise contamination, which poses challenges for traditional signal processing methods. In this study, we propose the use of entropy metrics to evaluate the dynamic complexity and irregularity of surface electromyographic (EMG) signals of patients assisted with mechanical ventilation. Shannon entropy and spectral entropy were computed to analyze EMG signals from two surface diaphragm channels recorded in mechanically ventilated patients during extubation preparation. According to clinical criteria, the patients were classified into the successful group (GE) - 19 patients with successful extubation after 48 hours, and the failure group (GF) - 21 patients who required reconnection to the ventilator within 48 hours. sEMG signals were recorded using 5-channel surface electrodes placed around the diaphragm muscle. Shannon and spectral entropies were calculated using a 0.5-minute window with an overlap of 80%. The results presented a greater complexity of the EMG signal in the SG group. This group shows higher peaks in Shannon entropy and elevated spectral entropy values compared to the FG group. Channels 2 and 3 presented the largest statistically significant differences.Clinical Relevance- Analyzing diaphragm EMG signals using entropy metrics could improve patient outcomes by optimizing the timing of extubation. These metrics would serve as a key indicator of readiness for extubation, providing an objective basis for more informed clinical decision-making.

JTD Keywords: Aged, Airway extubation, Diaphragm, Electromyography, Entropy, Female, Humans, Male, Middle aged, Respiration, artificial, Signal processing, computer-assisted, Ventilator weaning

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

JTD Keywords: Automatic Data Processing, Databases, Factual, Electrocardiography, Humans, Models, Statistical, Respiration, Respiration, Artificial, Respiratory Insufficiency, Respiratory Mechanics, Respiratory Muscles, Signal Processing, Computer-Assisted, Time Factors, Ventilator Weaning, Ventilators, Mechanical, Work of Breathing