New signal processing algorithms for automated external defibrillators

[ES]La fibrilación ventricular (VF) es el primer ritmo registrado en el 40\,\% de las muertes súbitas por paro cardiorrespiratorio extrahospitalario (PCRE). El único tratamiento eficaz para la FV es la desfibrilación mediante una descarga eléctrica. Fuera del hospital, la descarga se administra mediante un desfibrilador externo automático (DEA), que previamente analiza el electrocardiograma (ECG) del paciente y comprueba si presenta un ritmo desfibrilable. La supervivencia en un caso de PCRE depende fundamentalmente de dos factores: la desfibrilación temprana y la resucitación cardiopulmonar (RCP) temprana, que prolonga la FV y por lo tanto la oportunidad de desfibrilación. Para un correcto análisis del ritmo cardiaco es necesario interrumpir la RCP, ya que, debido a las compresiones torácicas, la RCP introduce artefactos en el ECG. Desafortunadamente, la interrupción de la RCP afecta negativamente al éxito en la desfibrilación. En 2003 se aprobó el uso del DEA en pacientes entre 1 y 8 años. Los DEA, que originalmente se diseñaron para pacientes adultos, deben discriminar de forma precisa las arritmias pediátricas para que su uso en niños sea seguro. Varios DEAs se han adaptado para uso pediátrico, bien demostrando la precisión de los algoritmos para adultos con arritmias pediátricas, o bien mediante algoritmos específicos para arritmias pediátricas. Esta tesis presenta un nuevo algoritmo DEA diseñado conjuntamente para pacientes adultos y pediátricos. El algoritmo se ha probado exhaustivamente en bases de datos acordes a los requisitos de la American Heart Association (AHA), y en registros de resucitación con y sin artefacto RCP. El trabajo comenzó con una larga fase experimental en la que se recopilaron y clasificaron retrospectivamente un total de 1090 ritmos pediátricos. Además, se revisó una base de arritmias de adultos y se añadieron 928 nuevos ritmos de adultos. La base de datos final contiene 2782 registros, 1270 se usaron para diseñar el algoritmo y 1512 para validarlo. A continuación, se diseñó un nuevo algoritmo DEA compuesto de cuatro subalgoritmos. Estos subalgoritmos están basados en un conjunto de nuevos parámetros para la detección de arritmias, calculados en diversos dominios de la señal, como el tiempo, la frecuencia, la pendiente o la función de autocorrelación. El algoritmo cumple las exigencias de la AHA para la detección de ritmos desfibrilables y no-desfibrilables tanto en pacientes adultos como en pediátricos. El trabajo concluyó con el análisis del comportamiento del algoritmo con episodios reales de resucitación. En los ritmos que no contenían artefacto RCP se cumplieron las exigencias de la AHA. Posteriormente, se estudió la precisión del algoritmo durante las compresiones torácicas, antes y después de filtrar el artefacto RCP. Para suprimir el artefacto se utilizó un nuevo método desarrollado a lo largo de la tesis. Los ritmos desfibrilables se detectaron de forma precisa tras el filtrado, los no-desfibrilables sin embargo no.

Data analysis advances in marine science for fisheries management : supervised classification application

169 p. : il. col.

ATP Antagonizes Thrombin-Induced Signal Transduction through 12(S)-HETE and cAMP

15 p.

A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Evidence for classification of c.1852_1853AA > GC in MLH1 as a neutral variant for Lynch syndrome

Background: Lynch syndrome (LS) is an autosomal dominant inherited cancer syndrome characterized by early onset cancers of the colorectum, endometrium and other tumours. A significant proportion of DNA variants in LS patients are unclassified. Reports on the pathogenicity of the c.1852_1853AA>GC (p.Lys618Ala) variant of the MLH1 gene are conflicting. In this study, we provide new evidence indicating that this variant has no significant implications for LS. Methods: The following approach was used to assess the clinical significance of the p.Lys618Ala variant: frequency in a control population, case-control comparison, co-occurrence of the p.Lys618Ala variant with a pathogenic mutation, co-segregation with the disease and microsatellite instability in tumours from carriers of the variant. We genotyped p.Lys618Ala in 1034 individuals (373 sporadic colorectal cancer [CRC] patients, 250 index subjects from families suspected of having LS [revised Bethesda guidelines] and 411 controls). Three well-characterized LS families that fulfilled the Amsterdam II Criteria and consisted of members with the p.Lys618Ala variant were included to assess co-occurrence and co-segregation. A subset of colorectal tumour DNA samples from 17 patients carrying the p.Lys618Ala variant was screened for microsatellite instability using five mononucleotide markers. Results: Twenty-seven individuals were heterozygous for the p.Lys618Ala variant; nine had sporadic CRC (2.41%), seven were suspected of having hereditary CRC (2.8%) and 11 were controls (2.68%). There were no significant associations in the case-control and case-case studies. The p.Lys618Ala variant was co-existent with pathogenic mutations in two unrelated LS families. In one family, the allele distribution of the pathogenic and unclassified variant was in trans, in the other family the pathogenic variant was detected in the MSH6 gene and only the deleterious variant co-segregated with the disease in both families. Only two positive cases of microsatellite instability (2/17, 11.8%) were detected in tumours from p.Lys618Ala carriers, indicating that this variant does not play a role in functional inactivation of MLH1 in CRC patients. Conclusions: The p.Lys618Ala variant should be considered a neutral variant for LS. These findings have implications for the clinical management of CRC probands and their relatives.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Image-based delineation and classification of built heritage masonry

[EN]Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

Economic Valuation of Ecosystem Services: Conflicts in Classification

4 p.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.

Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

162 p.

Novel Evidence hat Attributing Affectively Salient Signal to Random Noise Is Associated with Psychosis

We wished to replicate evidence that an experimental paradigm of speech illusions is associated with psychotic experiences. Fifty-four patients with a first episode of psychosis (FEP) and 150 healthy subjects were examined in an experimental paradigm assessing the presence of speech illusion in neutral white noise. Socio-demographic, cognitive function and family history data were collected. The Positive and Negative Syndrome Scale (PANSS) was administered in the patient group and the Structured Interview for Schizotypy-Revised (SIS-R), and the Community Assessment of Psychic Experiences (CAPE) in the control group. Patients had a much higher rate of speech illusions (33.3% versus 8.7%, ORadjusted: 5.1, 95% CI: 2.3-11.5), which was only partly explained by differences in IQ (ORadjusted: 3.4, 95% CI: 1.4-8.3). Differences were particularly marked for signals in random noise that were perceived as affectively salient (ORadjusted: 9.7, 95% CI: 1.8-53.9). Speech illusion tended to be associated with positive symptoms in patients (ORadjusted: 3.3, 95% CI: 0.9-11.6), particularly affectively salient illusions (ORadjusted: 8.3, 95% CI: 0.7-100.3). In controls, speech illusions were not associated with positive schizotypy (ORadjusted: 1.1, 95% CI: 0.3-3.4) or self-reported psychotic experiences (ORadjusted: 1.4, 95% CI: 0.4-4.6). Experimental paradigms indexing the tendency to detect affectively salient signals in noise may be used to identify liability to psychosis.

Diseño y evaluación de métodos de supresión del artefacto causado en el ECG por las compresiones torácicas proporcionadas por el dispositivo mecánico LUCAS.

[Es]En este proyecto se analizan el diseño y la evaluación de dos métodos para la supresión de la interferencia generada por las compresiones torácicas proporcionadas por el dispositivo mecánico LUCAS, en el electrocardiograma (ECG) durante el masaje de resucitación cardiopulmonar. El objetivo es encontrar un método que elimine el artefacto generado en el ECG de una manera efectiva, que permita el diagnóstico fiable del ritmo cardiaco. Encontrar un método eficaz sería de gran ayuda para no tener que interrumpir el masaje de resucitación para el análisis correcto del ritmo cardiaco, lo que supondría un aumento en las probabilidades de resucitación. Para llevar a cabo el proyecto se ha generado una base de datos propia partiendo de registros de paradas cardiorrespiratorias extra-hospitalarias. Esta nueva base de datos contiene 410 cortes correspondientes a 86 pacientes, siendo todos los episodios de 30 segundos de duración y durante los cuales el paciente, recibe masaje cardiaco. Por otro lado, se ha desarrollado una interfaz gráfica para caracterizar los métodos de supresión del artefacto. Esta, muestra las señales del ECG, de impedancia torácica y del ECG tras eliminar el artefacto en tiempo. Mediante esta herramienta se han procesado los registros aplicando un filtro adaptativo y un filtro de coeficientes constantes. La evaluación de los métodos se ha realizado en base a la sensibilidad y especificidad del algoritmo de clasificación de ritmos con las señales ECG filtradas. La mayor aportación del proyecto, por tanto, es el desarrollo de una potente herramienta eficaz para evaluar métodos de supresión del artefacto causado en el ECG por las compresiones torácicas al realizar el masaje de resucitación cardiopulmonar, y su posterior diagnóstico. Un instrumento que puede ser implementado para analizar episodios de resucitación de cualquier tipo de procedencia y capaz de integrar nuevos métodos de supresión del artefacto.

Image-Based Delineation and Classification of Built Heritage Masonry

Fundacion Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.