Abstract-Analysis of respiratory muscles activity is an effective technique for the study of pulmonary diseases such as obstructive sleep apnea syndrome (OSAS). Respiratory diseases, especially those associated with changes in the mechanical properties of the respiratory apparatus, are often associated with disruptions of the normally highly coordinated contractions of respiratory muscles. Due to the complexity of the respiratory control, the assessment of OSAS related dysfunctions by linear methods are not sufficient. Therefore, the objective of this study was the detection of diagnostically relevant nonlinear complex respiratory mechanisms. Two aims of this work were: 1) to assess coordination of respiratory muscles contractions through evaluation of interactions between respiratory signals and myographic signals through nonlinear analysis by means of cross mutual information function (CMIF); 2) to differentiate between functioning of respiratory muscles in patients with OSAS and in normal subjects. Electromyographic (EMG) and mechanomyographic (MMG) signals were recorded from three respiratory muscles: genioglossus, sternomastoid and diaphragm. Inspiratory pressure and flow were also acquired. All signals were measured in eight patients with OSAS and eight healthy subjects during an increased respiratory effort while awake. Several variables were defined and calculated from CMIF in order to describe correlation between signals. The results indicate different nonlinear couplings of respiratory muscles in both populations. This effect is progressively more evident at higher levels of respiratory effort.

Analysis of the respiratory muscle activity is a promising technique for diagnosis of respiratory diseases, such as chronic obstructive pulmonary disease (COPD). The sternomastoid muscle (SMM) was selected to study the activity of respiratory muscles due to its accessibility in order to define a noninvasive analysis. The aims of this work are two: analyze the relationship between the SMM function and pulmonary obstruction, and study the influence of spectral estimator on frequency parameters related with the muscle activity. For the first goal, we propose the analysis of vibromyographic and electromyographic signals from the SMM to study the muscle function during two ventilatory tests. Activity of SMM was found by means of several indexes: root-mean-square (rms) values, mean and median frequencies, and ratio between high and low-frequency components. For the second goal, spectral analysis was performed by means of nonparametric methods: Correlogram and Welch periodogram, and parametric methods: autoregressive (AR), moving average (MA), and ARMA models. It is deduced that these indexes show muscle activity and certain fatigue of the SMM, whose muscle function depends on the level of pulmonary obstruction, and they depend a lot of spectral estimator being the more suitable an AR model with high order.

Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.

Dyslipidemia, the disorder of lipoprotein metabolism resulting in high lipid profile, is an important modifiable risk factor for coronary heart diseases. It is associated with more than four million worldwide deaths per year. Half of the children with dyslipidemia have hyperlipidemia during adulthood, and its prediction and screening are thus critical. We designed a new dyslipidemia diagnosis system. The sample size of 725 subjects (age 14.66 ± 2.61 years; 48% male; dyslipidemia prevalence of 42%) was selected by multistage random cluster sampling in Iran. Single nucleotide polymorphisms (rs1801177, rs708272, rs320, rs328, rs2066718, rs2230808, rs5880, rs5128, rs2893157, rs662799, and Apolipoprotein-E2/E3/E4), and anthropometric, life-style attributes, and family history of diseases were analyzed. A framework for classifying mixed-type data in imbalanced datasets was proposed. It included internal feature mapping and selection, re-sampling, optimized group method of data handling using convex and stochastic optimizations, a new cost function for imbalanced data and an internal validation. Its performance was assessed using hold-out and 4-foldcross-validation. Four other classifiers namely as supported vector machines, decision tree, and multilayer perceptron neural network and multiple logistic regression were also used. The average sensitivity, specificity, precision and accuracy of the proposed system were 93%, 94%, 94% and 92%, respectively in cross validation. It significantly outperformed the other classifiers and also showed excellent agreement and high correlation with the gold standard. A non-invasive economical version of the algorithm was also implemented suitable for low- and middle-income countries. It is thus a promising new tool for the prediction of dyslipidemia.

The mechanical ventilator settings in patients with respiratory diseases like chronic obstructive pulmonary disease (COPD) during episodes of acute respiratory failure (ARF) is not a simple task that in most cases is successful based on the experience of physicians. This paper describes an interactive tool based in mathematical models, developed to make easier the study of the interaction between a mechanical ventilator and a patient. It describes all stages of system development, including simulated ventilatory modes, the pathologies of interest and interaction between the user and the system through a graphical interface developed in Matlab and Simulink. The developed computational tool allows the study of most widely used ventilatory modes and its advantages in the treatment of different kind of patients. The graphical interface displays all variables and parameters in the common way of last generation mechanical ventilators do and it is totally interactive, making possible its use by clinical personal, hiding the complexity of implemented mathematical models to the user. The evaluation in different clinical simulated scenes adjusts properly with recent findings in mechanical ventilation scientific literature.

Abstract-An interesting approach to study pulmonary diseases is the analysis of the respiratory muscle activity by means of electromyographic (EMG) B. Signals and instrumentationFour EMG and two VMG signals were simultaneously recorded from three respiratory muscles: genioglossus, sternomastoid and diaphragm. A surface EMG signal of genioglossus muscle was recorded with two electrodes (AgAgCl) placed on the submental zone (GEN-SEMG). In the same area, an accelerometer (Entran EGA-10) was also placed to record VMG signal (GEN-VMG). In addition, genioglossus activity was also monitored by means of intraoral surface electrodes located below the tongue (GEN-EMG) [6]. Two surface electrodes and another accelerometer were placed on the sternomastoid muscle to record EMG (SMM-SEMG) and VMG (SMM-VMG) signals, respectively. Finally, surface EMG signal was recorded from the diaphragm (DIA-SEMG).The myographic signals were amplified and bandpass filtered using a multichannel analog amplifier. The selected bandwidths at -3 dB and the sampling frequencies were, respectively: 5-200 Hz, 500 Hz (VMG) and 5-400 Hz, 1000 Hz (EMG). C. Increased respiratory effortDuring the experiment subjects were in a supine position and breathed through a nose mask connected to a low-resistance respiratory nonrebreathing valve. The inspiratory port of the nonrebreathing valve was connected to the external source of a negative pressure.The experimental protocol consisted of breathing without external pressure for 5 min before the negative pressure was applied. The pressure was decreased at 90-second intervals each time by the value of -7cm H 2 O until the subject could no longer breath. The maximum pressure in absolute value reached by the subject was defined as maximum maintained pressure (MMP). For every subject, pressure in each step of the experiment was expressed as a Hz CARDIAC INTERFERENCE IN MYOGRAPHIC SIGNALS FROM DIFFERENT RESPIRATORY MUSCLES AND LEVELS OF ACTIVITY Abstract Subject Terms Report Classification unclassified Classification of this page unclassified Classification of Abstract unclassified Limitation of Abstract UUNumber of Pages 4

Current sleep analyses have used electroencephalography (EEG) to establish sleep intensity through linear and nonlinear measures. Slow wave activity (SWA) and entropy are the most commonly used markers of sleep depth. The purpose of this study is to evaluate changes in brain EEG connectivity during sleep in healthy subjects and compare them with SWA and entropy. Four different connectivity metrics: coherence (MSC), synchronization likelihood (SL), cross mutual information function (CMIF), and phase locking value (PLV), were computed focusing on their correlation with sleep depth. These measures provide different information and perspectives about functional connectivity. All connectivity measures revealed to have functional changes between the different sleep stages. The averaged CMIF seemed to be a more robust connectivity metric to measure sleep depth (correlations of 0.78 and 0.84 with SWA and entropy, respectively), translating greater linear and nonlinear interdependences between brain regions especially during slow wave sleep. Potential changes of brain connectivity were also assessed throughout the night. Connectivity measures indicated a reduction of functional connectivity in N2 as sleep progresses. The validation of connectivity indexes is necessary because they can reveal the interaction between different brain regions in physiological and pathological conditions and help understand the different functions of deep sleep in humans.

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