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 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.

Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.

The current gold standard assessment of human inspiratory muscle function involves using invasive measures of transdiaphragmatic pressure (Pdi) or crural diaphragm electromyography (oesEMGdi). Mechanomyography is a non-invasive measure of muscle vibration associated with muscle contraction. Surface electromyogram and mechanomyogram, recorded transcutaneously using sensors placed over the lower intercostal spaces (sEMGlic and sMMGlic respectively), have been proposed to provide non-invasive indices of inspiratory muscle activation, but have not been directly compared to gold standard Pdi and oesEMGdi measures during voluntary respiratory manoeuvres. To validate the non-invasive techniques, the relationships between Pdi and sMMGlic, and between oesEMGdi and sEMGlic were measured simultaneously in 12 healthy subjects during an incremental inspiratory threshold loading protocol. Myographic signals were analysed using fixed sample entropy (fSampEn), which is less influenced by cardiac artefacts than conventional root mean square. Strong correlations were observed between: mean Pdi and mean fSampEn |sMMGlic| (left, 0.76; right, 0.81), the time-integrals of the Pdi and fSampEn |sMMGlic| (left, 0.78; right, 0.83), and mean fSampEn oesEMGdi and mean fSampEn sEMGlic (left, 0.84; right, 0.83). These findings suggest that sMMGlic and sEMGlic could provide useful non-invasive alternatives to Pdi and oesEMGdi for the assessment of inspiratory muscle function in health and disease.

The objective of the present work was to detect and analyze wheezes by means of a highly sensitive time-frequency algorithm. Automatic measurements were compared with clinical auscultation for forced exhalation segments from 1.2 to 0 liters/second (l/s). Sensitivities between 100% and 71%, as a function of flow level related to wheezing segments detection, were achieved. Time-frequency wheeze parameters were measured for the flow range from 1.2 to 0.2 l/s. Wheezes were detected in both analyzed groups; asthmatics (N = 16) and control subjects (N = 15). Significant differences between groups were found for the mean number of wheezes detected at basal condition (p = 0.0003). Frequency parameter differences were also significant (0.0112 < p < 0.0307). All these parameters were also studied after applying a bronchodilator drug (Terbutaline). Significant differences between patient groups were found when studying the changes in the number of wheezes for each patient (p = 0.0195). Finally, limited bandwidth parameters, which measure the bronchodilator response, were also studied.

Accurate signal estimation by means of coherent averaging techniques needs temporal alignment methods. A known low-pass filtering effect is yielded when alignment errors are present. This is very critical in the estimation of low-level high-frequency potentials in high-resolution ECG analysis. A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented in this paper. A real signal and additive random noise for several signal-to-noise ratios (SNR's) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interference (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.

Abstract-Differentiatingnormal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.Index Terms-Asthma, continuous adventitious sounds (CAS), ensemble empirical mode decomposition (EEMD), instantaneous frequency (IF), respiratory sounds (RS), wheezes.

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Automatic procedures to classify the QRS complex are very useful in diagnosis of cardiac disfunctwns. In this work we present an adaptive system to extract, in real time, the features that characterize the QRS with the Hermite functions model.The adaptive system is based on the multiple-input adaptive linear combiner, where the primary input signal is the succession of the QRS complexes, and the reference inputs are the Hermite functions. The weight vector becomes an estimation of the coefficients that represent the QRS complex in the Hermite function base. To adapt these weights we w e the LMS algorithm. We have incorporated a procedure to aLiaptively estimate a width parameter (b) that best fits each QRS complex. We present applicatww of this system to classify QRS in case of ECG signals affected by the phenomenon of bigeminy and to detect ectopic beats using the b parameter. In both cases we have obtained correct pattern classification.