B. Giraldo

Universitat Politecnica de Catalunya, ESAII, Faculty Member

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Analysis of the nonlinear autodependencies of respiratory pattern in patients on weaning trials

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004

ABSTRACT Traditional time domain techniques of data analysis are often not sufficient to characte... more ABSTRACT Traditional time domain techniques of data analysis are often not sufficient to characterize the nonlinear dynamics of respiration. In this study, the respiratory pattern variability was analyzed using auto mutual information measures. These provide access to nonlinear statistical autodependencies of respiratory pattern variability. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of breathing duration, inspiratory time, fractional inspiratory time, tidal volume and mean inspiratory flow were analyzed. Different measures based on auto-mutual information were studied to characterize the respiratory pattern variability with regard to its complex organization.

Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2006

One of the challenges in intensive care is the process of weaning from mechanical ventilation. We... more One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features.

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Information flow to assess cardiorespiratory interactions in patients on weaning trials

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2006

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variabilit... more Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B.

Estimating respiratory pattern variability by symbolic dynamics

Methods of information in medicine, 2004

The traditional techniques of data analysis are often not sufficient to characterize the complex ... more The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern variability was analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels. Breath duration (T(TOT)) time series and the relation T(I)/T(TOT), that contains the influence of inspiratory time (T(I)), were considered. Length-3 words and 3 different symbols were proposed. The incidence of the overlapping tau and the parameter alpha were analyzed. From the breath duration time series, the distribution of words with probability of occurrence higher than 6% was concentrated on one word for low respiratory variability, whereas high variability was characterized by 4 words, presenting a statistically significant difference (p </= 0.0005). The probability occurrence of words "110" and "111" was also sign...

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Correntropy-based nonlinearity test applied to patients with chronic heart failure

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

In this study we propose the correntropy function as a discriminative measure for detecting nonli... more In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surro...

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Analysis of the respiratory flow cycle morphology in chronic heart failure patients applying principal components analysis

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

The study of flow cycle morphology provides new information about the breathing pattern. This stu... more The study of flow cycle morphology provides new information about the breathing pattern. This study proposes the characterization of cycle morphology in chronic heart failure patients (CHF) patients, with periodic (PB) and non-periodic breathing (nPB) patterns, and healthy subjects. Principal component analysis is applied to extract a respiratory cycle model for each time segment defined by a 30-s moving window. To characterize morphology of the model waveform, a number of parameters are extracted whose significance is evaluated in terms of the following three classification problems: CHF patients with either PB or nPB, CHF patients versus healthy subjects, and nPB patients versus healthy subjects. 26 CHF patients (8 with PB and 18 with non-periodic breathing pattern (nPB)) and 35 healthy subjects are studied. The results show that a respiratory cycle compressed in time characterizes PB patients, i.e., shorter inspiratory and expiratory periods, and higher dispersion of the maximum ...

Support Vector Machine Classification Applied on Weaning Trials Patients

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

One of the most frequent reasons for instituting mechanical ventilation is to decrease patient&am... more One of the most frequent reasons for instituting mechanical ventilation is to decrease patient&#39;s work of breathing. Many attempts have been made to increase the effectiveness of the evaluation of the respiratory pattern with the analysis of the respiratory signals. This work proposes a method for the study of the differences in respiratory pattern variability in patients on weaning trials. The proposed method is based on a support vector machine using 35 features extracted from the respiratory flow signal. In this paper, a group of 146 patients with mechanical ventilation were studied: group S of 79 patients with successful weaning trials and group F of 67 patients that failed to maintain spontaneous breathing and were reconnected. Applying a feature selection procedure based on the use of the support vector machine with a leave-one-out cross-validation, it was obtained 86.67% of well classified patients on group S and 73.34% on group F, using only 8 of the 35 features. Therefore, support vector machine can be a classification method of the respiratory pattern variability useful in the study of patients on weaning trials.

La Bioenginyeria a Catalunya

Optimazed Symbolic Dynamics approach for the analysis of the respiratory pattern

Patients classification on weaning trials using neural networks and wavelet transform

The determination of the optimal time of the patients in weaning trial process from mechanical ve... more The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.

Estudio de la información mutua en la variabilidad del patrón respiratorio

Non-Linear Analysis of the Respiratory Pattern

Biomechanical Systems Technology - Volume 1: Computational Methods, 2007

Medidas De La Variabilidad Del Patrón Respiratorio en Pacientes Con Asistencia Ventilatoria

Analysis of Respiratory Flow Signals in Chronic Heart Failure Patients with Periodic Breathing

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

In patients with chronic heart failure (CHF), oscillatory breathing pattern predicts poor prognos... more In patients with chronic heart failure (CHF), oscillatory breathing pattern predicts poor prognosis. This work proposes a method to identify the respiratory pattern to determine periodic breathing (PB), Cheyne-Stokes respiration (CSR) and non-periodic respiratory patterns (nPB) through the respiratory flow signal. 26 patients are studied, classified in G 1 (PB), G 2 (CSR) and G 3 (nPB). The flow signal is filtered and normalized, to obtain the positive envelope that describes the respiratory pattern. With this new signal some features are extracted through its power spectral density (PSD). An adaptive feature selection algorithm is applied before the linear and non linear classification applying Leave-one-out crossvalidation technique. The result obtained with linear classification was 93% using the relation between total energy and frequency interval (I 1 ), peak amplitude (ampp), peak frequency (fp), and the highest slope of the positive envelope's PSD (Slope max ). And the best result was obtained with non linear technique, with 100% correctly classified patients, using only two parameters, fp and Slope max .

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Joint Symbolic Dynamic Analysis of Cardiorespiratory Interactions in Patients on Weaning Trials

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005

Assessing autonomic control provides information about patho-physiological imbalances. Measures o... more Assessing autonomic control provides information about patho-physiological imbalances. Measures of variability of the cardiac interbeat duration RR(n) and the variability of the breath duration T&amp;amp;amp;amp;amp;amp;amp;amp;lt;inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;Tot&amp;amp;amp;amp;amp;amp;amp;amp;lt;/inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;(n) are sensitive to those changes. The interactions between RR(n) and T&amp;amp;amp;amp;amp;amp;amp;amp;lt;inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;Tot&amp;amp;amp;amp;amp;amp;amp;amp;lt;/inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;(n) are complex and strongly non-linear. A study of joint symbolic dynamics is presented as a new short-term non-linear analysis method to investigate these interactions in patients on weaning trials. 78 patients from mechanical ventilation are studied: Group A (patients that failed to maintain spontaneous breathing and were reconnected) and Group B (patients with successful trials). Using the concept of joint symbolic dynamics, cardiac and respiratory changes were transformed into a word series, and the probability of occurrence of each word type was calculated and compared between both groups. Significant differences were found in 13 words, and the most significant p&amp;amp;amp;amp;amp;amp;amp;amp;lt;inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;n&amp;amp;amp;amp;amp;amp;amp;amp;lt;/inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;(W&amp;amp;amp;amp;amp;amp;amp;amp;lt;inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;c010, r010&amp;amp;amp;amp;amp;amp;amp;amp;lt;/inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;): 0.0041 &amp;amp;amp;amp;amp;amp;amp;amp;amp;#177; 0.0036 (group A) against 0.0012 &amp;amp;amp;amp;amp;amp;amp;amp;amp;#177; 0.0024 (group B), p-value = 0.00001. The number of seldom occurring word types (forbidden words) also presents significant differences fw&amp;amp;amp;amp;amp;amp;amp;amp;lt;inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;cr&amp;amp;amp;amp;amp;amp;amp;amp;lt;/inf&amp;amp;amp;amp;amp;amp;amp;amp;gt;: 6.9 &amp;amp;amp;amp;amp;amp;amp;amp;amp;#177; 6.6 against 13.5 &amp;amp;amp;amp;amp;amp;amp;amp;amp;#177; 5.3, p-value = 0.00004. Joint symbolic dynamics provides an efficient non-linear representation of cardiorespiratory interactions that offers simple physiological interpretations.

Study of the respiratory pattern variability in patients during weaning trials

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004

Mechanical ventilators are used to provide life support in patients with respiratory failure. One... more Mechanical ventilators are used to provide life support in patients with respiratory failure. One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. The respiratory pattern was characterized by the following time series: inspiratory time (T(I)), expiratory time (T(E)), breath duration (T(Tot)), tidal volume (V(T)), fractional inspiratory time (T(I)/T(Tot)), mean inspiratory flow (V(T)/T(I)), respiratory frequency (f), and rapid shallow breathing index (f/V(T)). The variational activity of breathing was partitioned into autoregressive, periodic and white noise fractions. Patients with unsuccessful trial presented a tendency to higher values of gross variability of V(T)/T(I) and f/V(T), and lower values of T(I). The autocorrelation coefficients tended to present higher values for T(I), T(I)/T(Tot) and V(T)/T(I). During both successful and unsuccessful T-tube test uncorrelated random behavior constituted &amp;amp;amp;amp;amp;amp;gt; 75% of the variance of each time breath components and represented 50 to 70% in the breath component related to V(T). Correlated behavior represented 6 to 21% in time components and 28 to 50% in component related to V(T).

Arrhythmia diagnosis system: validation methodology

Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society, 1995

Presents the implementation of the validation stage of an automatic system for the diagnosis of c... more Presents the implementation of the validation stage of an automatic system for the diagnosis of cardiac arrhythmias based on the Minnesota Code. Exact diagnosis of cardiac arrhythmias implies some degree of uncertainty and to establish a gold standard is not feasible. This automatic system has been developed to provide assistance in the diagnosis of 51 pathologies of cardiac arrhythmias. The

Validation of an algorithm for atrial fibrillation and flutter diagnosis

Computers in Cardiology 1997, 1997

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Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009

The process of weaning from mechanical ventilation is one of the challenges in intensive care. 14... more The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main clust...

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Spectral analysis of the RR series and the respiratory flow signal on patients in weaning process

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010

A considerable number of patients in weaning process have problems to keep spontaneous breathing ... more A considerable number of patients in weaning process have problems to keep spontaneous breathing during the trial and after it. This study proposes to extract characteristic parameters of the RR series and respiratory flow signal according to the patients' condition in weaning test. Three groups of patients have been considered: 93 patients with successful trials (group S), 40 patients that failed to maintain spontaneous breathing (group F), and 21 patients who had successful weaning trials, but that had to be reintubated before 48 hours (group R). The characterization was performed using spectral analysis of the signals, through the power spectral density, cross power spectral density and Coherence method. The parameters were extracted on the three frequency bands (VLF, LF and HF), and the principal statistical differences between groups were obtained in bands of VLF and HF. The results show an accuracy of 76.9% in the classification of the groups S and F.

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