Luis Estrada

Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy

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 the 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 study, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to the 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 Pearsons 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.

Characterization of laplacian surface electromyographic signals during isometric contraction in biceps brachii

Surface electromyography (sEMG) is a noninvasive technique for monitoring the electrical activity produced by the muscles. Usually, sEMG is performed by carrying out monopolar or bipolar recordings by means of conventional Ag/AgCl electrodes. In contrast, Laplacian recordings of sEMG could also be obtained by using coaxial ring electrodes. Laplacian recordings increase spatial resolution and attenuate other distant bioelectric interferences. Nevertheless, the spectral characteristics of this kind of recordings have been scarcely studied. The objective of this paper is to characterize the sEMG signals recorded with a Laplacian ring electrode and to compare them with traditional bipolar recordings with disc electrodes. Both kinds of signals were collected simultaneously in two healthy subjects during resting and sustained isometric voluntary contraction activities in biceps brachii. The conducted study computed the cumulative percentage of the power spectrum of sEMG so as to determine the energy bandwidth of the two kinds of recordings and the signal to noise ratio in different bands of the sEMG spectrum. Also, muscle fatigue, a condition when muscle force is reduced, was assessed using indexes from amplitude and frequency domain. The results of this study suggest that Laplacian sEMG has higher spectral bandwidth but a lower signal to noise ratio in comparison to bipolar sEMG. In addition, frequency fatigue indexes showed that Laplacian recording had better response than bipolar recording, which suggests that Laplacian electrode can be useful to study muscular fatigue due to better spatial resolution.

Respiratory signal derived from the smartphone built-in accelerometer during a Respiratory Load Protocol.

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RRMmg) was compared with that measured from inspiratory pressure signal (RRP). Results showed a Pearsons correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RRmmg and RRP measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

EMG-derived respiration signal using the fixed sample entropy during an Inspiratory load protocol

Extracting clinical information from one single measurement represents a step forward in the assessment of the respiratory muscle function. This attracting idea entails the reduction of the instrumentation and fosters to develop new medical integrated technologies. We present the use of the fixed sample entropy (fSampEn) as a more direct method to non-invasively derive the breathing activity from the diaphragm electromyographic (EMGdi) signal, and thus to extract the respiratory rate, an important vital sign which is cumbersome and time-consuming to be measured by clinicians. fSampEn is a method to evaluate the EMGdi activity that is less sensitive to the cardiac activity (ECG) and its application has proven to be useful to evaluate the load of the respiratory muscles. The behavior of the proposed method was tested in signals from two subjects that performed an inspiratory load protocol, which consists of increments in the inspiratory mouth pressure (Pmouth). Two respiratory signals were derived and compared to the Pmouth signal: the ECG-derived respiration (EDR) signal from the lead-I configuration, and the EMG-derived respiration (EMGDR) signal by applying the fSampEn method over the EMGdi signal. The similitude and the lag between signals were calculated through the cross-correlation between each derived respiratory signal and the Pmouth. The EMGDR signal showed higher correlation and lower lag values (≥ 0.91 and ≤ 0.70 s, respectively) than the EDR signal (≥ 0.83 and ≤0.99 s, respectively). Additionally, the respiratory rate was estimated with the Pmouth, EDR and EMGDR signals showing very similar values. The results from this preliminary work suggest that the fSampEn method can be used to derive the respiration waveform from the respiratory muscle electrical activity.

Evaluation of Laplacian diaphragm electromyographic recording in a dynamic inspiratory maneuver

The analysis of the electromyographic signal of the diaphragm muscle (EMGdi) can provide important information for evaluating the respiratory muscular function. The EMGdi can be recorded using surface Ag/AgCl disc electrodes in monopolar or bipolar configuration. However, these non-invasive EMGdi recordings are usually contaminated by the electrocardiographic (ECG) signal. EMGdi signal can also be noninvasively recorded using concentric ring electrodes in bipolar configuration (CRE) that estimate Laplacian surface potential. Laplacian recordings increase spatial resolution and attenuate distant bioelectric interferences, such as the ECG. Thus, the objective of this work is to compare and to evaluate CRE and traditional bipolar EMGdi recordings in a healthy subject during a dynamic inspiratory maneuver with incremental inspiratory loads. In the conducted study, it was calculated the cumulative percentage of power spectrum of EMGdi recordings to determine the signal bandwidth, and the power ratio between the EMGdi signal segments with and without cardiac activity. The results of this study suggest that EMGdi acquired with CRE electrodes is less affected by the ECG interference, achieves a wider bandwidth and a higher power ratio between segments without cardiac activity and with cardiac activity.

Influence of Parameter Selection in Fixed Sample Entropy of Surface Diaphragm Electromyography for Estimating Respiratory Activity

Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the present study were to evaluate the influence of the embedding dimension m, the tolerance value r, the size of the moving window, and the sampling frequency, and to establish recommendations for estimating the respiratory activity when using the fSampEn on surface EMGdi recorded for different inspiratory efforts. Values of m equal to 1 and r ranging from 0.1 to 0.64, and m equal to 2 and r ranging from 0.13 to 0.45, were found to be suitable for evaluating respiratory activity. fSampEn was less affected by window size than classical amplitude parameters. Finally, variations in sampling frequency could influence fSampEn results. In conclusion, the findings suggest the potential utility of fSampEn for estimating muscle respiratory effort in further sleep studies.

Evaluating respiratory muscle activity using a wireless sensor platform

Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearsons correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearsons correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.

Time-frequency representations of the sternocleidomastoid muscle electromyographic signal recorded with concentric ring electrodes

The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight timefrequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born- Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.

Evaluation of indirect measures of neural inspiratory time from invasive and noninvasive recordings of respiratory activity

Measuring diaphragmatic electromyography (EMGdi) provides an indirect quantification of neural respiratory drive and allows the delimitation of diaphragm neural activation and deactivation during inspiration. EMGdi recordings have been incorporated in novel modes of assisted mechanical ventilation, such as neurally adjusted ventilatory assist (NAVA), to trigger and cycle-off the ventilator. The EMGdi signal improves the assistance delivered by more conventional ventilatory modes, in which the ventilator is synchronized with the patient employing a pneumatic triggering. In this work, we evaluate the time delay between the onset and offset of inspiratory activity estimated from EMGdi and three respiratory mechanical signals: the respiratory flow (FL), the transdiaphragmatic pressure (Pdi) and the diaphragm length (Ldi) signals. To this purpose, these signals were acquired in three mongrel dogs surgically instrumented under general anesthesia. Onsets and offsets were estimated manually and by automatic algorithms on these signals. The highest delays were obtained between EMGdi and FL (100 ms) while the lowest delays were obtained between EMGdi and Pdi (8 ms). Moreover, differences between manual and automatic estimations showed a mean absolute error lower than 45 ms. In conclusion, our study points out that both EMGdi and Pdi signals detect the onset and offset of inspiratory activity earlier than the FL signal, and would therefore be better for the improvement of patient-ventilator synchrony.