Publications

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

Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. Traditionally, the EMGdi amplitude has been estimated using the average rectified value (ARV) and the root mean square (RMS). In this work, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG). The proposed method was tested in synthetic and recorded EMGdi signals. fSampEn was less sensitive to the effect of cardiac activity on EMGdi signals with different levels of NRD than ARV and RMS amplitude parameters. The mean and standard deviation of the Pearson’s correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38???0.12, 0.27???0.11 and 0.11???0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83???0.02, 0.76???0.07 and 0.61???0.19, respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.

JTD Keywords: Electromyography, diaphragm muscle, neural respiratory drive

The analysis of the electromyographic signal of the diaphragm muscle (EMGdi) can provide important information in order to evaluate the respiratory muscular function. However, EMGdi signals are usually contaminated by the electrocardiographic (ECG) signal. An adaptive noise cancellation (ANC) based on event-synchronous cancellation can be used to reduce the ECG interference in the recorded EMGdi activity. In this paper, it is proposed an ANC scheme for cancelling the ECG interference in EMGdi signals using only the EMGdi signal (without acquiring the ECG signal). In this case the detection of the QRS complex has been performed directly in the EMGdi signal, and the ANC algorithm must be robust to false or missing QRS detections. Furthermore, an automatic criterion to select the adaptive constant of the LMS algorithm has been proposed (μ). The μ constant is selected automatically so that the canceling signal energy equals the energy of the reference signal (which is an estimation of the ECG interference present in the EMGdi signal). This approach optimizes the tradeoff between cancellation of ECG interference and attenuation of EMG component. A number of weights equivalent of a time window that contains several QRS complexes is selected in order to make the algorithm robust to QRS detection errors.

JTD Keywords: Adaptive Canceller, EMG, Diaphragm muscle

Diaphragm electromyography (EMGdi) provides important information on diaphragm activity, to detect neuromuscular disorders of the most important muscle in the breathing inspiratory phase. EMGdi is habitually recorded using needles or esophageal catheters, with the implication of being invasive for patients. Surface electrodes offer an alternative for the non-invasive assessment of diaphragm activity. Ag/AgCl surface disc electrodes are used in monopolar or bipolar configuration to record EMGdi signals. On the other hand, Laplacian surface potential can be estimated by signal recording through active concentric ring electrodes. This kind of recording could reduce physiological interferences, increase the spatial selectivity and reduce orientation problems in the electrode location. The aim of this work is to compare EMGdi signals recorded simultaneously with disc electrodes in bipolar configuration and a Laplacian ring electrode over chest wall. EMGdi signal was recorded in one healthy subject during a breath hold maneuver and a static inspiratory maneuver based on Mueller’s technique. In order to estimate the covered frequency range and the degree of noise contamination in both bipolar and Laplacian EMGdi signals, the cumulative percentage of the power spectrum and the signal to noise ratio in sub-bands were determined. Furthermore, diaphragm fatigue was evaluated by means of amplitude and frequency parameters. Our findings suggest that Laplacian EMGdi recording covers a broader frequency range although with higher noise contamination compared to bipolar EMGdi recording. Finally, in Laplacian recording fatigue indexes showed a clearer trend for muscle fatigue detection and also a reduced cardiac interference, providing an alternative to bipolar recording for diaphragm fatigue studies.

JTD Keywords: Laplacian electrode, Diaphragm muscle, Fatigue, Surface electromyography

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

JTD Keywords: Adaptive filters, Frequency modulation, Interference, Muscles, Noise cancellation, Vibrations, Cardiology, Medical signal processing, Muscle, Signal denoising, ANC algorithm, MCG interference, Pearson correlation coefficient, Adaptive noise cancellation, Cardiac vibration interference, Cardiac vibration noise, Diaphragm muscle, Mechanocardiographic signal, Mechanomyographic signals, Respiratory muscles effort