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MEET: Motif Elements Estimation Toolki

Title: MEET: Motif Elements Estimation Toolki Authors: Pairó Castiñeira, Erola; Maynou Fernández, Joan; Vallverdú Ferrer, Montserrat; Gallardo Chacón, Joan Josep; Caminal Magrans, Pere; Marco Colás, Santiago; Perera Lluna, Alexandre Abstract: MEET (Motif Elements Estimation Toolkit) es un paquete en R que integra un conjunto de algoritmos para la detección computacional de los puntos de unión de los factores de transcripción (TFBS). El paquete en R MEET incluye cinco programas de búsqueda de motivos: MEME/MAST (Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) y Match. Además, permite al usuario trabajar con diferentes algoritmos de alineamiento múltiple: MUSCLE (Multiple Sequence Comparison by Log-Expectation), ClustalW y MEME. El paquete puede trabajar en dos modos diferentes, entrenamiento y detección. El modo entrenamiento permite escoger los parámetros óptimos del detector escogido. Y el modo detección permite, una vez escogidos los parámetros, analizar un genoma en busca de puntos de unión. Además, ambos modos pueden combinar los diferentes métodos de alineamiento y de detección, permitiendo al usuario un amplio abanico de posibilidades. Esta característica permite comparar los diferentes métodos computacionales al mismo nivel,sin realizar ningún agravio comparativo debido al alineamiento.

Odour mapping under strong backgrounds with a metal oxide sensor array

2013-05-17T07:42:53Z

Title: Odour mapping under strong backgrounds with a metal oxide sensor array Authors: Ziyatdinov, Andrey; Blanco Calvo, Jose Maria; Bermudez Badia, Sergi; Lechon, Miguel; Marco, Santiago; Verschure, Paul; Perera Lluna, Alexandre Abstract: This text describes the data from an initial set of navigation experiments in the scope of the Bio-ICT European project NEUROCHEM (...) This poster shows that a preprocessing based on Independent Component Analysis is able to discriminate two odour sources. Further work will include automatic determination of the number of components present in the tunnel and the application of the Neurochem platform in surge-and-cast behavioral models.

SVM-based feature selection to optimize sensitivity–specificity balance applied to weaning

2013-04-25T11:51:52Z

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%.

Validation of the qCO cardiac output monitor during Valsalva maneuver

2013-04-16T12:35:03Z

Title: Validation of the qCO cardiac output monitor during Valsalva maneuver Authors: Jospin, Mathieu; Aguilar, J.P.; Gambus, Pedro L.; Jensen, Erik W.; Vallverdú Ferrer, Montserrat; Caminal Magrans, Pere Abstract: Monitoring cardiac output for a variety of patient conditions is essential to ensure tissue perfusion and oxygenation. Cardiac output can be measured either invasively using a pulmonary artery catheter or non-invasively using impedance cardiography (ICG). The objective of the present study was to validate a cardiac output monitor, the qCO (Quantium Medical, Barcelona, Spain). The qCO is based on the ICG principle. Twenty-five volunteers (18-75 years) were enrolled in the study. The duration of the study was 10 min. The subjects were asked to rest quietly in an armchair for a duration of 5 min. At 5 min they were asked to do a Valsalva maneuver which is known to decrease the cardiac output. The baseline value of the normalized cardiac output (qCO index) was compared with the minimum value during the Valsalva maneuver. The results showed (t-test, p<0.0005) significant difference between the cardiac output estimated at baseline and during the Valsalva maneuver. In conclusion, the qCO was able to indicate trend changes of the cardiac output in volunteers.

A software tool for large-scale synthetic experiments based on polymeric sensor arrays

2013-03-05T18:34:30Z

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

Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins

2013-02-28T16:22:47Z

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

Biologically inspired computation for chemical sensing

2013-02-28T15:42:29Z

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

Electromiografía de superficie multicanal como herramienta no invasiva en la rehabilitación neuromuscular

2013-02-26T11:22:49Z

Title: Electromiografía de superficie multicanal como herramienta no invasiva en la rehabilitación neuromuscular Authors: Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel Abstract: La señal EMG de superficie permite analizar cuantitativamente los cambios fisiológicos ocasionados por diferentes patologías ya sea sobre la Médula espinal, las Motoneuronas, la unión neuromuscular o los músculos propiamente dichos. Por tratarse de una técnica no invasiva, facilita el proceso de diagnóstico y monitorización de dichas enfermedades. Por otra parte, la EMG multicanal permite estudiar directamente los determinantes fisiológicos de la fatiga muscular, relacionados con cambios a nivel celular que ocasionan variaciones en la conducción de los potenciales de acción sobre las fibras musculares. En este estudio se introducen los mecanismos de la contracción muscular, su relación con la señal EMG y se presentan dos ejemplos de aplicación en el estudio de patologías de la extremidad superior.

A synchronization system for the analysis of biomedical signals recorded with different devices from mechanically ventilated patients

2013-02-26T08:45:44Z

Title: A synchronization system for the analysis of biomedical signals recorded with different devices from mechanically ventilated patients Authors: Camacho, Alejandro; Hernández Valdivieso, Alher Mauricio; Londoño, Zulma; Serna Higuita, Leidy Yanet; Mañanas Villanueva, Miguel Ángel Abstract: Conducting research associated with mechanically ventilated patients often requires the recording of several biomedical signals to dispose of multiple sources of information to perform a robust analysis. This is especially important in the analysis of the relationship between pressure, volume and flow, signals available from mechanical ventilators, and other biopotentials such as the electromyogram of respiratory muscles, intrinsically related with the ventilatory process, but not commonly recorded in the clinical practice. Despite the usefulness of recording signals from multiple sources, few medical devices include the possibility of synchronizing its data with other provided by different biomedical equipment and some may use inaccurate sampling frequencies. Even thought a variant or inaccurate sampling rate does not affect the monitoring of critical patients, it restricts the study of simultaneous related events useful in research of respiratory system activity. In this article a device for temporal synchronization of signals recorded from multiple biomedical devices is described as well as its application in the study of patients undergoing mechanical ventilation with research purposes

Classification of mental tasks using different spectral estimation methods

2013-02-18T15:29:32Z

Title: Classification of mental tasks using different spectral estimation methods Authors: Diez, Pablo F.; Laciar, Eric; Mut, Vicente; Ávila, Enrique; Torres Cebrián, Abel

Evaluación de diferentes algoritmos adaptativos para la atenuación de la interferencia cardiaca en señales mecanomiográficas simuladas

2013-02-18T14:50:17Z

Title: Evaluación de diferentes algoritmos adaptativos para la atenuación de la interferencia cardiaca en señales mecanomiográficas simuladas Authors: Torres Cebrián, Abel; Sarlabous Uranga, Leonardo; Fiz Fernández, José A.; Jané Campos, Raimon Abstract: El estudio de la señal mecanomiográfica del músculo diafragma (MMGdi) es una técnica utilizada para evaluar el esfuerzo muscular respiratorio. El estudio de la relación entre los parámetros de amplitud y frecuencia de esta señal con el esfuerzo respiratorio realizado es de gran interés para investigadores y médicos debido a su potencial de diagnóstico sobre la función muscular respiratoria. Las señales MMGdi se ven afectas por una componente interferente correspondiente a la actividad vibratoria cardíaca o interferencia mecanocardiográfica (MCG). Para reducir o atenuar esta actividad se puede utilizar una cancelación adaptativa de interferencias (CAI). En este trabajo se ha evaluado el esquema de CAI propuesto mediante una señal MMGdi sintética generada con amplitud y frecuencia controlada a la que se le ha añadido ruido MCG real adquirido durante apnea. El coeficiente de correlación de Pearson (r) entre la amplitud y la frecuencia teóricas, y la amplitud y la frecuencia evaluadas mediante el RMS y la frecuencia media del espectro, respectivamente, disminuye considerablemente cuando se añade el ruido cardíaco a la señal MMGdi sintética: pasa de 0.95 a 0.87 para la amplitud, y de 0.97 a 0.76 para la frecuencia. Con los algoritmos de CAI propuestos el efecto del ruido MCG sobre la actividad MMGdi se reduce considerablemente (r de 0.93 para la amplitud y 0.97 para la frecuencia media). El método de CAI propuesto en este trabajo es una técnica adecuada para atenuar la interferencia MCG en señales MMGdi.

Reducción de interferencia cardíaca en señales MMG diafragmáticas de un protocolo de carga incremental sostenida mediante filtrado adaptativo RLS

2013-02-18T14:37:42Z

Title: Reducción de interferencia cardíaca en señales MMG diafragmáticas de un protocolo de carga incremental sostenida mediante filtrado adaptativo RLS Authors: Sarlabous Uranga, Leonardo; Torres Cebrián, Abel; Fiz Fernández, José A.; Jané Campos, Raimon Abstract: En este trabajo se aplicó el filtrado adaptativo empleando el algoritmo RLS para reducir la interferencia de origen cardíaco en las señales mecanomiográficas diafragmáticas (MMGdi) registradas durante un protocolo de carga incremental sostenida. La señal MMGdi fue dividida en tramos con y sin ruido cardíaco, CRC y SRC, respectivamente. En cada tramo se estudio el comportamiento de la densidad espectral de potencia (DEP), y los parámetros de amplitud RMS y ARV para cada una de las cargas inspiratorias que conforman el test. Los resultados obtenidos, empleando filtro adaptativo de orden =50, con el algoritmo RLS y valores de - = 1, permiten reducir considerablemente la interferencia cardíaca en las señales MMGdi.

Evaluation and adaptive attenuation of the cardiac vibration interference in mechanomyographic signals

2013-02-18T12:32:39Z

Title: Evaluation and adaptive attenuation of the cardiac vibration interference in mechanomyographic signals Authors: Sarlabous Uranga, Leonardo; Torres Cebrián, Abel; Fiz, José Antonio; Morera Prat, Josep Maria; Jané Campos, Raimon Abstract: The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson’s correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

Symbolic dynamics of QT interval series in ischemic cardiomyopathy

2013-02-14T18:54:55Z

Title: Symbolic dynamics of QT interval series in ischemic cardiomyopathy Authors: Cuponne, Anna; Vallverdú Ferrer, Montserrat; Gomis Román, Pedro; Porta, Alberto; Voss, Andreas; Bayes de Luna, Antonio; Caminal Magrans, Pere Abstract: Repolarization dynamics may be of increasing interest in analyzing ECG-Holter for characterization of myocardial ischemic events related to cardiac death. The quantification of the dynamics of the beat-to-beat QT interval fluctuations, representing changes in repolarization duration, may be another emerging marker of cardiac events. Based on these arguments, we propose a symbolic analysis series to quantify the dynamics of the beat-to-beat QT interval fluctuations, representing changes in repolarization duration, and the prevalence of sympathetic or parasympathetic cardiac modulation in the RR series. This analysis decomposes the series in patterns of length L=3 beat and classify them into three categories: non-variable, variable, and very variable patterns referred to as P0, P1 and P2 patterns. The present work analyses QT and RR series obtained from 24-hour ECG-Holter recordings in order to obtained patterns able to stratify high (HRG) and low risk (LRG) of suffer cardiac mortality in patients with symptomatic myocardial ischemia. Comparing LRG and HRG, results showed that pattern P0 could better quantify QT series and pattern P2 the RR series. These findings suggest a decreased cardiac vagal function with a relative increase in sympathetic cardiac modulation, and more complex pattern of ventricular repolarization in the HRG.

Outlier detection in high-density surface electromyographic signals

2013-02-14T11:02:50Z

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.