http://hdl.handle.net/2117/3759 Sun, 26 May 2013 04:27:32 GMT 2013-05-26T04:27:32Z webmaster.bupc@upc.edu Universitat Politècnica de Catalunya. Servei de Biblioteques i Documentació no http://hdl.handle.net/2117/18985 Title: SVM-based feature selection to optimize sensitivity–specificity balance applied to weaning Authors: Garde, Ainara; Voss, Andreas; Caminal Magrans, Pere; Benito Vales, Salvador; Giraldo Giraldo, Beatriz Abstract: Classification algorithms with unbalanced data sets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine(SVM)-based optimized features election method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index(B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients’ weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analys is to cardiac interbeat and breath durations. First, the most suitable parameters (C þ ,C ,s)are selected to define the appropriate SVM. Then, the features election process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%. Thu, 25 Apr 2013 11:51:52 GMT http://hdl.handle.net/2117/18985 2013-04-25T11:51:52Z Garde, Ainara; Voss, Andreas; Caminal Magrans, Pere; Benito Vales, Salvador; Giraldo Giraldo, Beatriz no Support vectormachines Balance index Sensitivity–specificity balance Cardiorespiratory interaction Joint symbolicdynamics Feature selection Weaning procedure Classification algorithms with unbalanced data sets tend to produce high predictive accuracy over the majority class, but poor predictive accuracy over the minority class. This problem is very common in biomedical data mining. This paper introduces a Support Vector Machine(SVM)-based optimized features election method, to select the most relevant features and maintain an accurate and well-balanced sensitivity–specificity result between unbalanced groups. A new metric called the balance index(B) is defined to implement this optimization. The balance index measures the difference between the misclassified data within each class. The proposed optimized feature selection is applied to the classification of patients’ weaning trials from mechanical ventilation: patients with successful trials who were able to maintain spontaneous breathing after 48 h and patients who failed to maintain spontaneous breathing and were reconnected to mechanical ventilation after 30min. Patients are characterized through cardiac and respiratory signals, applying joint symbolic dynamic (JSD) analys is to cardiac interbeat and breath durations. First, the most suitable parameters (C þ ,C ,s)are selected to define the appropriate SVM. Then, the features election process is carried out with this SVM, to maintain B lower than 40%. The best result is obtained using 6 features with an accuracy of 80%, a B of 18.64%, a sensitivity of 74.36% and a specificity of 82.42%. http://hdl.handle.net/2117/18081 Title: A software tool for large-scale synthetic experiments based on polymeric sensor arrays Authors: Ziyatdinov, Andrey; Fernandez Diaz, Eduard; Chaudry, A.; Marco Colás, Santiago; Persaud, K.; Perera Lluna, Alexandre Tue, 05 Mar 2013 18:34:30 GMT http://hdl.handle.net/2117/18081 2013-03-05T18:34:30Z Ziyatdinov, Andrey; Fernandez Diaz, Eduard; Chaudry, A.; Marco Colás, Santiago; Persaud, K.; Perera Lluna, Alexandre no http://hdl.handle.net/2117/18023 Title: Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins Authors: Jaramillo-Garzón, Jorge Alberto; Gallardo Chacón, Joan Josep; Castellanos Domínguez, Germán; Perera Lluna, Alexandre Thu, 28 Feb 2013 16:22:47 GMT http://hdl.handle.net/2117/18023 2013-02-28T16:22:47Z Jaramillo-Garzón, Jorge Alberto; Gallardo Chacón, Joan Josep; Castellanos Domínguez, Germán; Perera Lluna, Alexandre no http://hdl.handle.net/2117/18021 Title: Biologically inspired computation for chemical sensing Authors: Fonollosa, Jordi; Gutierrez-Galvez, Agustin; Lansner, Anders; Martinez, Dominique; Rospars, Jean Piere; Beccherelli, Romeo; Perera Lluna, Alexandre; Pearce, Tim; Vershure, Paul; Persaud, K.; Marco Colás, Santiago Thu, 28 Feb 2013 15:42:29 GMT http://hdl.handle.net/2117/18021 2013-02-28T15:42:29Z Fonollosa, Jordi; Gutierrez-Galvez, Agustin; Lansner, Anders; Martinez, Dominique; Rospars, Jean Piere; Beccherelli, Romeo; Perera Lluna, Alexandre; Pearce, Tim; Vershure, Paul; Persaud, K.; Marco Colás, Santiago no http://hdl.handle.net/2117/17757 Title: Outlier detection in high-density surface electromyographic signals Authors: Marateb, HR; Rojas Martínez, Mónica; Mansourian, Marjan; Merletti, R.; Mañanas Villanueva, Miguel Ángel Abstract: Recently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause nearzero single differential signals when using gel. Such signals are called ‘outliers’ in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify ‘bad’ channels. The overall performance of this method was tested using the agreement rate against three experts’ opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising. Thu, 14 Feb 2013 11:02:50 GMT http://hdl.handle.net/2117/17757 2013-02-14T11:02:50Z Marateb, HR; Rojas Martínez, Mónica; Mansourian, Marjan; Merletti, R.; Mañanas Villanueva, Miguel Ángel no Recently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause nearzero single differential signals when using gel. Such signals are called ‘outliers’ in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify ‘bad’ channels. The overall performance of this method was tested using the agreement rate against three experts’ opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising. http://hdl.handle.net/2117/17750 Title: Identification of isometric contractions based on High Density EMG maps Authors: Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel; Alonso López, Joan Francesc; Merletti, R. Abstract: Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve patternrecognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it. The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the ‘‘isolated masses’’ method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation. Thu, 14 Feb 2013 09:49:16 GMT http://hdl.handle.net/2117/17750 2013-02-14T09:49:16Z Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel; Alonso López, Joan Francesc; Merletti, R. no Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve patternrecognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it. The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the ‘‘isolated masses’’ method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation. http://hdl.handle.net/2117/17746 Title: High-density surface EMG maps from upper-arm and forearm muscles Authors: Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel; Alonso López, Joan Francesc Abstract: Background sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. Methods An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze differences due to type of task and effort level. Results Significant differences were observed between EMG signal power obtained from single bipolar configuration and HD-EMG and better results regarding the identification of tasks and effort levels were obtained with the latter. Additionally, average maps for a population of 12 subjects were obtained and differences in the co-activation pattern of muscles were found not only from variables related to the intensity of the maps but also to their spatial distribution. Conclusions Intensity and spatial distribution of HD-EMG maps could be useful in applications where the identification of movement intention and its strength is needed, for example in robotic-aided therapies or for devices like powered- prostheses or orthoses. Finally, additional data transformations or other features are necessary in order to improve the performance of tasks identification. Thu, 14 Feb 2013 08:06:10 GMT http://hdl.handle.net/2117/17746 2013-02-14T08:06:10Z Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel; Alonso López, Joan Francesc no Background sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. Methods An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze differences due to type of task and effort level. Results Significant differences were observed between EMG signal power obtained from single bipolar configuration and HD-EMG and better results regarding the identification of tasks and effort levels were obtained with the latter. Additionally, average maps for a population of 12 subjects were obtained and differences in the co-activation pattern of muscles were found not only from variables related to the intensity of the maps but also to their spatial distribution. Conclusions Intensity and spatial distribution of HD-EMG maps could be useful in applications where the identification of movement intention and its strength is needed, for example in robotic-aided therapies or for devices like powered- prostheses or orthoses. Finally, additional data transformations or other features are necessary in order to improve the performance of tasks identification. http://hdl.handle.net/2117/16053 Title: Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam Authors: Alonso López, Joan Francesc; Mañanas Villanueva, Miguel Ángel; Romero Lafuente, Sergio; Rojas Martínez, Mónica; Riba Serrano, Jordi Abstract: Rationale Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes. Objectives To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches. Methods EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence. Results Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study. Conclusions Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions. Fri, 15 Jun 2012 11:01:36 GMT http://hdl.handle.net/2117/16053 2012-06-15T11:01:36Z Alonso López, Joan Francesc; Mañanas Villanueva, Miguel Ángel; Romero Lafuente, Sergio; Rojas Martínez, Mónica; Riba Serrano, Jordi no Rationale Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes. Objectives To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches. Methods EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence. Results Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study. Conclusions Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions. http://hdl.handle.net/2117/15827 Title: Interfaces y sistemas en rehabilitación y compensación funcional para la autonomía personal y la terapia clínica Authors: Ceres, Ramón; Mañanas Villanueva, Miguel Ángel; Azorín, José María Abstract: La Bioingeniería constituye un área de trabajo e investigación multidisciplinar entre las ingenierías y la medicina que resulta de un interés humano, social y económico creciente. La automática en particular, en sus aspectos de percepción, modelado, control, monitorización, actuación e interacción, entre otros, ofrece importantes conocimientos y herramientas para abordar los problemas relacionados con el diagnóstico y el seguimiento de patologías, con las necesidades funcionales especiales e igualmente con las diferentes terapias a aplicar. Este tutorial presenta aspectos relacionados con el estado del arte y últimos avances en los siguientes campos: Interfaces para la interacción y comunicación de personas con discapacidad, robótica para la rehabilitación y compensación funcional, y sistemas para la mejora de la terapia clínica Fri, 11 May 2012 09:13:06 GMT http://hdl.handle.net/2117/15827 2012-05-11T09:13:06Z Ceres, Ramón; Mañanas Villanueva, Miguel Ángel; Azorín, José María no La Bioingeniería constituye un área de trabajo e investigación multidisciplinar entre las ingenierías y la medicina que resulta de un interés humano, social y económico creciente. La automática en particular, en sus aspectos de percepción, modelado, control, monitorización, actuación e interacción, entre otros, ofrece importantes conocimientos y herramientas para abordar los problemas relacionados con el diagnóstico y el seguimiento de patologías, con las necesidades funcionales especiales e igualmente con las diferentes terapias a aplicar. Este tutorial presenta aspectos relacionados con el estado del arte y últimos avances en los siguientes campos: Interfaces para la interacción y comunicación de personas con discapacidad, robótica para la rehabilitación y compensación funcional, y sistemas para la mejora de la terapia clínica http://hdl.handle.net/2117/14908 Title: Virtual laboratory for simulation and learning of cardiovascular system function in BME studies Authors: Hernández Valdivieso, Alher Mauricio; Salazar Sánchez, Maria Bernarda; Urrego Higuita, David Alexander; Costa Castelló, Ramon; Mañanas Villanueva, Miguel Ángel Abstract: The application of engineering system analysis is a very important field in biomedical engineering (BME) studies: modeling, simulation and control of the most important physiological systems. A virtual laboratory for the analysis and the study of human circulatory system is presented in this paper. This laboratory is based on the compilation of several mathematical models described in the literature. In addition, some model parameters have been tuned by means of experimental data under caffeine stimulus. The computational tool has been built using MATLAB/SIMULINK and EJS, so it combines good computation capabilities with interactivity. The virtual laboratory has been designed in order to understand the operation of the circulatory system under normal conditions, and to predict circulatory variables at different types and levels of stimuli and conditions. Wed, 01 Feb 2012 12:32:07 GMT http://hdl.handle.net/2117/14908 2012-02-01T12:32:07Z Hernández Valdivieso, Alher Mauricio; Salazar Sánchez, Maria Bernarda; Urrego Higuita, David Alexander; Costa Castelló, Ramon; Mañanas Villanueva, Miguel Ángel no The application of engineering system analysis is a very important field in biomedical engineering (BME) studies: modeling, simulation and control of the most important physiological systems. A virtual laboratory for the analysis and the study of human circulatory system is presented in this paper. This laboratory is based on the compilation of several mathematical models described in the literature. In addition, some model parameters have been tuned by means of experimental data under caffeine stimulus. The computational tool has been built using MATLAB/SIMULINK and EJS, so it combines good computation capabilities with interactivity. The virtual laboratory has been designed in order to understand the operation of the circulatory system under normal conditions, and to predict circulatory variables at different types and levels of stimuli and conditions. http://hdl.handle.net/2117/13147 Title: Graph theory-based measures as predictors of gene morbidity Authors: Massanet Vila, Raimon; Caminal Magrans, Pere; Perera Lluna, Alexandre Abstract: Previous studies have suggested that some graph properties of protein interaction networks might be related with gene morbidity. In particular, it has been suggested that when a polymorphism affects a gene, it is more likely to produce a disease if the node degree in the interaction network is higher than for other genes. However, these results do not take into account the possible bias introduced by the variance in the amount of information available for different genes. This work models the relationship between the morbidity associated with a gene and the degrees of the nodes in the protein interaction network controlling the amount of information available in the literature. A set of 7461 genes and 3665 disease identifiers reported in the Online Mendelian Inheritance in Man (OMIM) was mined jointly with 9630 nodes and 38756 interactions of the Human Proteome Resource Database (HPRD). The information available from a gene was measured through PubMed mining. Results suggest that the correlation between the degree of a node in the protein interaction network and its morbidity is largely contributed by the information available from the gene. Even though the results suggest a positive correlation between the degree of a node and its morbidity while controlling the information factor, we believe this correlation has to be taken with caution for it can be affected by other factors not taken into account in this study. Wed, 31 Aug 2011 12:31:18 GMT http://hdl.handle.net/2117/13147 2011-08-31T12:31:18Z Massanet Vila, Raimon; Caminal Magrans, Pere; Perera Lluna, Alexandre no Previous studies have suggested that some graph properties of protein interaction networks might be related with gene morbidity. In particular, it has been suggested that when a polymorphism affects a gene, it is more likely to produce a disease if the node degree in the interaction network is higher than for other genes. However, these results do not take into account the possible bias introduced by the variance in the amount of information available for different genes. This work models the relationship between the morbidity associated with a gene and the degrees of the nodes in the protein interaction network controlling the amount of information available in the literature. A set of 7461 genes and 3665 disease identifiers reported in the Online Mendelian Inheritance in Man (OMIM) was mined jointly with 9630 nodes and 38756 interactions of the Human Proteome Resource Database (HPRD). The information available from a gene was measured through PubMed mining. Results suggest that the correlation between the degree of a node in the protein interaction network and its morbidity is largely contributed by the information available from the gene. Even though the results suggest a positive correlation between the degree of a node and its morbidity while controlling the information factor, we believe this correlation has to be taken with caution for it can be affected by other factors not taken into account in this study. http://hdl.handle.net/2117/12080 Title: Segmented Symbolic Dynamics for Risk Stratification in Patients with Ischemic Heart Failure, Cardiovascular Engineering and Technology Authors: Voss, Andreas; Schroeder, Rico; Caminal Magrans, Pere; Vallverdú Ferrer, Montserrat; Brunel, Helena; Cygankiewicz, I.; Vázquez, Rafael; Bayes de Luna, Antonio Abstract: Chronic heart failure (CHF) is recognized as major and escalating public health problem. Approximately 69% of CHF patients suffer from cardiac death within 5 years after the initial diagnosis. Until now, no generally accepted ECG risk predictors in CHF patients are available. The objective of this study was to investigate the suitability of the new developed non-linear method segmented symbolic dynamics (SSD) for risk stratification in patients with ischemic cardiomyopathy (ICM) in comparison to other indices from time and frequency domain, non-linear dynamics, and clinical markers. Twenty-four hour Holter ECGs were recorded from 256 ICM patients. Heart rate variability (HRV) was analyzed from the filtered beat-to-beat interval time series. For calculating SSD, NN interval time series were segmented in 1 min overlapping windows with a window length of 30 min. For each window a symbol- and word-transformation was performed and probabilities of word type occurrences were calculated. Several indices from frequency domain and non-linear dynamics revealed high univariate significant differences (p<0.01) discriminating low (n = 221) and high risk ICM patients (n = 35). For multivariate risk stratification in ICM patients the two optimal mixed parameter sets consisting of either two clinical and three non-clinical indices (two from SSD) or three clinical and two non-clinical indices (one from SSD) achieved 74 and 75% sensitivity and 79 and 76% specificity, respectively. These results suggest that the new SSD enhances considerably risk stratification in ICM patients. The multivariate analysis including SSD leads to an optimum accuracy of 81%. Fri, 25 Mar 2011 16:05:48 GMT http://hdl.handle.net/2117/12080 2011-03-25T16:05:48Z Voss, Andreas; Schroeder, Rico; Caminal Magrans, Pere; Vallverdú Ferrer, Montserrat; Brunel, Helena; Cygankiewicz, I.; Vázquez, Rafael; Bayes de Luna, Antonio no Chronic heart failure (CHF) is recognized as major and escalating public health problem. Approximately 69% of CHF patients suffer from cardiac death within 5 years after the initial diagnosis. Until now, no generally accepted ECG risk predictors in CHF patients are available. The objective of this study was to investigate the suitability of the new developed non-linear method segmented symbolic dynamics (SSD) for risk stratification in patients with ischemic cardiomyopathy (ICM) in comparison to other indices from time and frequency domain, non-linear dynamics, and clinical markers. Twenty-four hour Holter ECGs were recorded from 256 ICM patients. Heart rate variability (HRV) was analyzed from the filtered beat-to-beat interval time series. For calculating SSD, NN interval time series were segmented in 1 min overlapping windows with a window length of 30 min. For each window a symbol- and word-transformation was performed and probabilities of word type occurrences were calculated. Several indices from frequency domain and non-linear dynamics revealed high univariate significant differences (p<0.01) discriminating low (n = 221) and high risk ICM patients (n = 35). For multivariate risk stratification in ICM patients the two optimal mixed parameter sets consisting of either two clinical and three non-clinical indices (two from SSD) or three clinical and two non-clinical indices (one from SSD) achieved 74 and 75% sensitivity and 79 and 76% specificity, respectively. These results suggest that the new SSD enhances considerably risk stratification in ICM patients. The multivariate analysis including SSD leads to an optimum accuracy of 81%. http://hdl.handle.net/2117/11218 Title: MEG Connectivity Analysis in Patients with Alzheimer’s Disease Using Cross Mutual Information and Spectral Coherence Authors: Alonso López, Joan Francesc; Poza Crespo, Jesús; Mañanas Villanueva, Miguel Ángel; Romero Lafuente, Sergio; Fernández Lucas, Alberto; Hornero Sánchez, Roberto Wed, 26 Jan 2011 15:33:47 GMT http://hdl.handle.net/2117/11218 2011-01-26T15:33:47Z Alonso López, Joan Francesc; Poza Crespo, Jesús; Mañanas Villanueva, Miguel Ángel; Romero Lafuente, Sergio; Fernández Lucas, Alberto; Hornero Sánchez, Roberto no http://hdl.handle.net/2117/10437 Title: Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal Authors: Garde Martínez, Ainara; Sörnmo, Leif; Jané Campos, Raimon; Giraldo Giraldo, Beatriz Abstract: This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects. Mon, 29 Nov 2010 10:23:41 GMT http://hdl.handle.net/2117/10437 2010-11-29T10:23:41Z Garde Martínez, Ainara; Sörnmo, Leif; Jané Campos, Raimon; Giraldo Giraldo, Beatriz no This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects. http://hdl.handle.net/2117/10135 Title: An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas Authors: Morgenstern de Muller, Christian Rudolf; Schwaibold, Matthias; Randerath, Winfried J.; Bolz, Armin; Jané Campos, Raimon Abstract: The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifierwith nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted fromthe Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas. Fri, 05 Nov 2010 09:53:57 GMT http://hdl.handle.net/2117/10135 2010-11-05T09:53:57Z Morgenstern de Muller, Christian Rudolf; Schwaibold, Matthias; Randerath, Winfried J.; Bolz, Armin; Jané Campos, Raimon no The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifierwith nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted fromthe Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.