다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This paper shows an adaptive statistical test for QRS detection of ECG signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The previous algorithms based on maximum a posteriori (MAP) estimation result in high signal model complexity which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. A simplified model based on the independent Gaussian properties of the DFT coefficients is proposed. This model allows to define a simplified MAP probability function and to define an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. Moreover, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.

A simplified QRS decision stage based on the DFT coefficients

The emerging applications of wireless speech communication are demanding increasing levels of performance in noise adverse environments together with the design of high response rate speech processing systems. This is a serious obstacle to meet the demands of modern applications and therefore these systems often needs a noise reduction algorithm working in combination with a precise voice activity detector (VAD). This paper presents a new voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm defines an optimum likelihood ratio test (LRT) involving Multiple and correlated Observations (MO) and assuming a jointly Gaussian probability density function (jGpdf). An analysis of the methodology for N = {2, 3} shows the robustness of the proposed approach by means of a clear reduction of the classification error as the number of observations is increased. The algorithm is also compared to different VAD methods including the G.729, AMR and AFE standards, as well as recently reported algorithms showing a sustained advantage in speech/non-speech detection accuracy and speech recognition performance.

An efficient VAD based on a hang-over scheme and a likelihood ratio test

En este trabajo presentamos los últimos trabajos realizados conjuntamente por los grupos SiPBA y BioSiP, donde se han desarrollado diferentes métodos basados en aprendizaje estadı́stico y Deep Learning para el procesamiento de imágenes cerebrales en diferentes modalidades. Los métodos desarrollados se han aplicado al diagnóstico precoz de enfermedades neurodegenerativas, como la enfermedad de Alzheimer y la enfermedad de Parkinson, proporcionando diferentes representaciones de las imágenes y mejorando las tasas de clasificacón de otros sistemas CAD previamente publicados.

Juan Manuel Górriz

Papers

A simplified QRS decision stage based on the DFT coefficients

An efficient VAD based on a hang-over scheme and a likelihood ratio test

Procesamiento, análisis y clasificación de neuroimagen con arquitecturas Deep Learning híbridas