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

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance - This establishes the characterization of success and failure patients in the extubation process. © 2021 IEEE.

JTD Keywords: classification, recognition, Airway extubation, Artificial ventilation, Clinical practices, Clinical process, Diaphragm, Diaphragm muscle, Diaphragms, Electrodes, Electromyographic, Extubation, Frequency domain analysis, Human, Humans, Maximum differences, Mechanical ventilation, New protocol, Respiration, artificial, Respiratory system, Risk of failure, Spontaneous breathing, Surface electrode, Surface emg signals, Thorax, Ventilation, Ventilator weaning