Comparison of parametric hidden semi-Markov models for analysing the different phases of cardiac cycle

Ο καρδιακός κύκλος μπορεί να χωριστεί σε διαφορετικές φάσεις λειτουργίας που διακρίνονται στις φάσεις συστολής, διαστολής αλλά και των ενδιάμεσων χρόνων που μεσολαβούν μεταξύ αυτών των δύο φάσεων. Με τη βοήθεια ηλεκτρικών και μαγνητικών σημάτων γίνεται προσπάθεια ακριβούς πρόβλεψης των χρόνων μετάβασης αυτών των σταδίων και τα κρυμμένα ημι-μαρκοβιανά μοντέλα έχουν προταθεί ως υποψήφια στατιστικά μοντέλα για καλή πρόβλεψη. Στη διπλωματική αυτή συγκρίνονται διαφορετικά παραμετρικά ημι-μαρκοβιανά μοντέλα ως προς την ικανότητα πρόβλεψης τους στη βάση κάποιων πραγματικών δεδομένων. Η εργασία αυτή επεκτείνει αποτελέσματα που παρουσιάστηκαν στη διπλωματική της Κ. Καταχάνα "Signal Processing and Statistical Analysis of the Cardiac Cycle via hidden Semi-Markov Models ".The cardiac cycle can be broken down into different phases, these are the phase of S_1 sound, Systole, S_2 sound, Diastole. So modeling this heart cycle periodicity through an Hidden Semi Markov Model gives very important information about the health status of the heart and can be used to detect abnormalities in its operation. Using electric and magnetic signals we make an effort to accurately predict the sojourn times of cardiac cycle phases suggesting also the Hidden Semi-Markov models appropriate to predict. In this thesis, different parametric Hidden Semi-Markov models are compared in their predictive ability and this is done using real data.This thesis extends the results are presented in the thesis of Konstantina Katachana "Signal Processing and Statistical Analysis of the Cardiac Cycle via hidden Semi-Markov models"

Hacia la implementación on the edge de un segmentador de PCG basado en la U-Net

A computer-aided cardiovascular diseases diagnostic system requires both accuracy and real-time response. This can be reached thanks to the deep model implementation on edge devices. This works introduces a reduction strategy for a heart sound segmentation model, and its impact in the implementation over low-spec FPGAs.Un sistema de asistencia al diagnóstico de enfermedades cardiovasculares requiere de precisión y respuesta en tiempo real, algo que se puede alcanzar gracias a la implementación de modelos deep learning on the edge. En este trabajo se presenta la reducción de un modelo para la segmentación de fonocardiogramas y su efecto en la implementación sobre FPGAs low-spec

Synthesis of normal and abnormal heart sounds using Generative Adversarial Networks

En esta tesis doctoral se presentan diferentes métodos propuestos para el análisis y síntesis de sonidos cardíacos normales y anormales, logrando los siguientes aportes al estado del arte: i) Se implementó un algoritmo basado en la transformada wavelet empírica (EWT) y la energía promedio normalizada de Shannon (NASE) para mejorar la etapa de segmentación automática de los sonidos cardíacos; ii) Se implementaron diferentes técnicas de extracción de características para las señales cardíacas utilizando los coeficientes cepstrales de frecuencia Mel (MFCC), los coeficientes de predicción lineal (LPC) y los valores de potencia. Además, se probaron varios modelos de Machine Learning para la clasificación automática de sonidos cardíacos normales y anormales; iii) Se diseñó un modelo basado en Redes Adversarias Generativas (GAN) para generar sonidos cardíacos sintéticos normales. Además, se implementa un algoritmo de eliminación de ruido utilizando EWT, lo que permite una disminución en la cantidad de épocas y el costo computacional que requiere el modelo GAN; iv) Finalmente, se propone un modelo basado en la arquitectura GAN, que consiste en refinar señales cardíacas sintéticas obtenidas por un modelo matemático con características de señales cardíacas reales. Este modelo se ha denominado FeaturesGAN y no requiere una gran base de datos para generar diferentes tipos de sonidos cardíacos. Cada uno de estos aportes fueron validados con diferentes métodos objetivos y comparados con trabajos publicados en el estado del arte, obteniendo resultados favorables.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods