L'estudi del senyal electrocardiogràfic (ECG) obtingut mitjançant elèctrodes de superfície és una prova mèdica freqüent que té una gran significació en el diagnòstic de l'activitat cardíaca. Des de la incorporació dels ordinadors als equips d'obtenció i enregistrament de l'ECG, que va provocar un gran impacte en la tecnologia i disseny d'aquests equips, l'anàlisi automàtica del senyal electrocardiogràfic ha estat un problema abordable. Cal precisar, però, que l'anàlisi assistit per computadora de l'ECG convencional no aporta per se nova informació per al diagnòstic respecte a l'observada directament per un cardiòleg.L'Electrocardiografia d'alta resolució (ECGAR) és un camp de recerca recent. Consisteix en la detecció i quantificació de potencials cardíacs de baixa amplitud, que no es poden captar amb els procediments de l'ECG convencional. L'ECGRA requereix el desenvolupament de tècniques de processat digital en el domini temporal i la realització de programes que permetin l'obtenció automàtica de l'activitat cardíaca de baixa amplitud. A més a més cal, per captar el senyal, la utilització d'una instrumentació biomèdica molt superior en prestacions a l'emprada habitualment. Els treballs presentats fins ara efectuen l'estimació dels potencials cardíacs vinculats a les ones d'amplitud més gran dins l'electrocardiograma. La majoria de les realitzacions existents són només aplicables a les ones d'elevada relació senyal-soroll. Aquesta restricció del problema no ha evitat certes deficiències en la qualitat de les estimacions obtingudes. Així s'ha arribat a una baixa coincidència de resultats en certs estudis mèdics comparatitus. Les contribucions existents en aquest tema s'han pres com a referència i punt de partida d'aquesta tesi. El treball presentat en aquesta tesi és una aportació en el camp del processat digital de senyals electrocardiogràfics, emmarcat especialment en l'ECG d'alta resolució. En el primer capítol es fa una descripció detallada del problema, presentant les característiques del senyal i la metodologia per a la seva obtenció. En aquest sentit s'ha proposat i desenvolupat una estructura modular del sistema de processat, on s'han fet contribucions tant en els mètodes emprats com en l'aplicació. En el segon capítol es presenta el tractament de l'ECG d'alta resolució com un problema en el camp dels processos aleatoris. Es descriu la tècnica emprada per a l'estimació dels potencials cardíacs, modelats com la part determinista del procés. Segons aquest enfocament es proposa una estructura del sistema de processat per a l'anàlisi del senyal, que ve desenvolupada en els posteriors capítols. El tercer capítol està dedicat a la detecció d'ones dins del senyal ECG, malgrat les seves variacions al llarg del temps i la contaminació per soroll d'origen biològic o extern. S'ha desenvolupat un sistema de detecció particularitzat per als complexos QRS, que són el conjunt d'ones associades a l'activitat de la contracció ventricular de cada batec cardíac. El sistema proposat s'ha mostrat molt robust, detectant les ones en tot tipus de situacions. Es puntualitzen les aportacions per a aquest detector, tant en el disseny com en l'aplicació. La seva contribució resulta molt útil per a les posteriors etapes de processat.En el quart capítol es presenten uns mètodes d'alineament de senyals orientats al cas dels ECG. En principi es fa una descripció general del problema, descrivint tot seguit els mètodes proposats amb un enfocament original. A continuació s'efectua un estudi de les prestacions d'aquests mètodes en simulació, així com aplicats a senyals reals. Finalment es descriuen els criteris d'aplicabilitat en situacions reals, comprovant el bon funcionament fins i tot per a senyals de baixa relació senyal-soroll. El cinquè capítol està específicament orientat al processat de l'ECG d'alta resolució. Es presenta la metodologia seguida, que també inclou les etapes proposades i descrites en capítols anteriors. A continuació es mostra l'aplicació d'aquestes tècniques a l'obtenció de potencials cardíacs de gran interès mèdic, com són els potencials ventriculars tardans, o diferents potencials vinculats a altres ones de l'ECG. L'aplicació dels mètodes i les etapes de processat presentades per a l'obtenció i tractament de l'ECG d'alta resolució han permès la recuperació de potencials cardíacs de forma fiable, tot i aplicant-lo a senyals de baixa relació senyal-soroll.

El objetivo de esta tesis se enmarca en el diseño y desarrollo de un sistema automático de ayuda al diagnostico de arritmias cardiacas, denominado Saydac, que comprende: la caracterización del ECG, que permite generar su descripción en forma cualitativa y cuantitativa; la detección y el diagnostico de la fibrilación y el fluter auriculares a través de la detección de las ondas F del registro electrocardiográfico; la descripción y codificación de las arritmias cardiacas basado en el código Minnesota, y la validación de Saydac para la cual se han obtenido los diagnósticos de 8 evaluadores (3 médicos cardiólogas expertos en arritmias, 3 médicos cardiólogos, 1 medico no cardiólogo y Saydac) para un total de 100 señales electrocardiográficas efectuándose luego un análisis estadístico de las diferentes distancias entre los diagnostico de cada uno de los evaluadores. Se ha constatado que Saydac responde satisfactoriamente a los objetivos de su diseño, con diagnósticos más próximos a los médicos cardiólogos especialistas en arritmias que al resto de evaluadores.

Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, finger temperature, electrocardiogram (ECG), electromyography signal (EMG), skin conductance signal (SC) etc. followed up by a careful analysis of the acquired signals. The accuracy is totally dependent on the experience of an expert. Again the number of such experts is also very limited. Heart rate is considered as an important parameter in determining stress. It reflects status of the autonomic nervous system (ANS) and thus is very effective in monitoring any imbalance in patient’s stress level. Therefore, a computer-aided system is useful to determine stress level based on various features that can be extracted from a patient’s heart rate signals. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Again, ECG signal is frequently contaminated by outliers produced by the loose conduction of the electrode due to sneezing, itching etcetera that hampers the value of the features. A Case-Based Reasoning (CBR) System is helpful when it is really hard to formulate rule and the knowledge on the domain is also weak. A CBR system is developed to evaluate how closely it can diagnose stress levels compare to an expert. A study is done to find out mostly used features to reduce the number of features used in the system and in case library. A software prototype is developed that can collect ECG signal from a patient through ECG sensor and calculate Inter Beat Interval (IBI) signal and features from it. Instead of doing manual visual inspection a new way to remove outliers from the IBI signal is also proposed and implemented here. The case base has been initiated with 22 reference cases classified by an expert. A performance analysis has been done and the result considering how close the system can perform compare to the expert is presented. On the basis of the evaluations an accuracy of 86% is obtained compare to an expert. However, the correctly classified case for stressed group (Sensitivity) was 57% and it is quite important to increase as it is related to the safety issue of health. The reasons of relatively lower sensitivity and possible ways to improve it are also investigated and explained.

Based on the acquisition trajectory, PROPELLER MRI repeatedly sampled the center k-space in every blade, which was used to provide most of the energy of an image. The purpose of view sharing PROPELLER is to improve the spatio-temporal resolution of dynamic imaging by reducing the acquisition time of single frame to that of single blade. With the center k-space provided by only one blade, which is called the target blade, the high spatial-frequency components were appropriately contributed by a set of neighboring blades with different rotation angles, leading to the high spatial resolution after reconstruction. In this study, a flow phantom experiment with the injection of T1-shortening Gd-DTPA solution was performed to exam the feasibility and accuracy of view-sharing PROPELLER. Furthermore, cardiac imaging of healthy volunteer obtained by the proposed technique was also done with ECG gating to test the image quality without any injection of contrast agent. The in-vivo experiment was done with and without breath holding. In addition to slight aliasing artifact due to insufficient FOV, no other artifact was observed.

ECG measures electrical potentials on the body surface via contact electrodes. Conditions such as movement of the patient, breathing, and interaction between the electrodes and skin cause baseline wandering of the ECG signal. Baseline wandering noise can mask some important features of the ECG signal / hence it is desirable to remove this noise for proper analysis of the ECG signal. This study includes an implementation and evaluation of methods to remove this noise, such as finite impulse response filters, infinite impulse response filters, interpolation filters and adaptive filters. These filters are first applied offline to simulated ECG data. The filter outputs and their frequency spectra are compared to the pure ECG signal and its frequency spectrum using visual inspection and numerical evaluation criteria such as root mean squared error and percentage root relative squared error. The best filters are then selected and applied online to the same simulated data. Finally, these best methods are used to suppress the baseline wandering noise in real ECG recordings using both offline and online filtering. In the offline application, windowing type filters were found to be the most successful filters among the implemented filters. However, a high filter order should be used to produce such good results, which increases the computation time, thus it may not be the best method for online filtering, in which fast computation is essential. Butterworth bidirectional type is preferred for online filtering since it has lower computational complexity, and it produces acceptable results.

Conventional electrocardiography (ECG) is an essential tool for investigating cardiac disorders such as arrhythmias or myocardial infarction. It consists of interpretation of potentials recorded at the body surface that occur due to the electrical activity of the heart. However, electrical signals originated at the heart suffer from attenuation and smoothing within the thorax, therefore ECG signal measured on the body surface lacks some important details. The goal of forward and inverse ECG problems is to recover these lost details by estimating the heart&amp / #8217 / s electrical activity non-invasively from body surface potential measurements. In the forward problem, one calculates the body surface potential distribution (i.e. torso potentials) using an appropriate source model for the equivalent cardiac sources. In the inverse problem of ECG, one estimates cardiac electrical activity based on measured torso potentials and a geometric model of the torso. Due to attenuation and spatial smoothing that occur within the thorax, inverse ECG problem is ill-posed and the forward model matrix is badly conditioned. Thus, small disturbances in the measurements lead to amplified errors in inverse solutions. It is difficult to solve this problem for effective cardiac imaging due to the ill-posed nature and high dimensionality of the problem. Tikhonov regularization, Truncated Singular Value Decomposition (TSVD) and Bayesian MAP estimation are some of the methods proposed in literature to cope with the ill-posedness of the problem. The most common approach in these methods is to ignore temporal relations of epicardial potentials and to solve the inverse problem at every time instant independently (column sequential approach). This is the fastest and the easiest approach / however, it does not include temporal correlations. The goal of this thesis is to include temporal constraints as well as spatial constraints in solving the inverse ECG problem. For this purpose, two methods are used. In the first method, we solved the augmented problem directly. Alternatively, we solve the problem with column sequential approach after applying temporal whitening. The performance of each method is evaluated.

Electrocardiography (ECG) is the most important noninvasive tool used for diagnosing heart diseases. An ECG interpretation program can help the physician state the diagnosis correctly and take the corrective action. Detection of the QRS complexes from the ECG signal is usually the first step for an interpretation tool. The main goal in this thesis was to develop robust and high performance QRS detection algorithms, and using the results of the QRS detection step, to classify these beats according to their different pathologies. In order to evaluate the performances, these algorithms were tested and compared in Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database, which was developed for research in cardiac electrophysiology. In this thesis, four promising QRS detection methods were taken from literature and implemented: a derivative based method (Method I), a digital filter based method (Method II), Tompkin&rsquo / s method that utilizes the morphological features of the ECG signal (Method III) and a neural network based QRS detection method (Method IV). Overall sensitivity and positive predictivity values above 99% are achieved with each method, which are compatible with the results reported in literature. Method III has the best overall performance among the others with a sensitivity of 99.93% and a positive predictivity of 100.00%. Based on the detected QRS complexes, some features were extracted and classification of some beat types were performed. In order to classify the detected beats, three methods were taken from literature and implemented in this thesis: a Kth nearest neighbor rule based method (Method I), a neural network based method (Method II) and a rule based method (Method III). Overall results of Method I and Method II have sensitivity values above 92.96%. These findings are also compatible with those reported in the related literature. The classification made by the rule based approach, Method III, did not coincide well with the annotations provided in the MIT-BIH database. The best results were achieved by Method II with the overall sensitivity value of 95.24%.

The main goal in inverse and forward problems of electrocardiography (ECG) is to better understand the electrical activity of the heart. In the forward problem of ECG, one obtains the body surface potential (BSP) distribution (i.e., the measurements) when the electrical sources in the heart are assumed to be known. The result is a mathematical model that relates the sources to the measurements. In the inverse problem of ECG, the unknown cardiac electrical sources are estimated from the BSP measurements and the mathematical model of the torso. Inverse problem of ECG is an ill-posed problem, and regularization should be applied in order to obtain a good solution. Tikhonov regularization is a well-known method, which introduces a trade-off between how well the solution fits the measurements and how well the constraints on the solution are satisfied. This trade-off is controlled by a regularization parameter, which can be easily calculated by the L-curve method. It is theoretically possible to include more than one constraint in the cost function / however finding more than one regularization parameter to use with each constraint is a challenging problem. It is the aim of this thesis to use genetic algorithm (GA) optimization method to obtain regularization parameters to solve the inverse ECG problem when multiple constraints are used for regularization. The results are presented when there are two spatial constraints, when there is one spatial, one temporal constraint, and when there are two spatial one temporal constraints / the performances of these three applications are compared to Tikhonov regularization results and to each other. As a conlcusion, it is possible to obtain correct regularization parameters using the GA method, and using more than one constraints yields improvements in the results.

La compression des signaux ECG trouve encore plus d'importance avec le développement de la télémédecine. En effet, la compression permet de réduire considérablement les coûts de la transmission des informations médicales à travers les canaux de télécommunication. Notre objectif dans ce travail de thèse est d'élaborer des nouvelles méthodes de compression des signaux ECG à base des polynômes orthogonaux. Pour commencer, nous avons étudié les caractéristiques des signaux ECG, ainsi que différentes opérations de traitements souvent appliquées à ce signal. Nous avons aussi décrit de façon exhaustive et comparative, les algorithmes existants de compression des signaux ECG, en insistant sur ceux à base des approximations et interpolations polynomiales. Nous avons abordé par la suite, les fondements théoriques des polynômes orthogonaux, en étudiant successivement leur nature mathématique, les nombreuses et intéressantes propriétés qu'ils disposent et aussi les caractéristiques de quelques uns de ces polynômes. La modélisation polynomiale du signal ECG consiste d'abord à segmenter ce signal en cycles cardiaques après détection des complexes QRS, ensuite, on devra décomposer dans des bases polynomiales, les fenêtres de signaux obtenues après la segmentation. Les coefficients produits par la décomposition sont utilisés pour synthétiser les segments de signaux dans la phase de reconstruction. La compression revient à utiliser un petit nombre de coefficients pour représenter un segment de signal constitué d'un grand nombre d'échantillons. Nos expérimentations ont établi que les polynômes de Laguerre et les polynômes d'Hermite ne conduisaient pas à une bonne reconstruction du signal ECG. Par contre, les polynômes de Legendre et les polynômes de Tchebychev ont donné des résultats intéressants. En conséquence, nous concevons notre premier algorithme de compression de l'ECG en utilisant les polynômes de Jacobi. Lorsqu'on optimise cet algorithme en supprimant les effets de bords, il dévient universel et n'est plus dédié à la compression des seuls signaux ECG. Bien qu'individuellement, ni les polynômes de Laguerre, ni les fonctions d'Hermite ne permettent une bonne modélisation des segments du signal ECG, nous avons imaginé l'association des deux systèmes de fonctions pour représenter un cycle cardiaque. Le segment de l'ECG correspondant à un cycle cardiaque est scindé en deux parties dans ce cas: la ligne isoélectrique qu'on décompose en séries de polynômes de Laguerre et les ondes P-QRS-T modélisées par les fonctions d'Hermite. On obtient un second algorithme de compression des signaux ECG robuste et performant.

Η Τηλεϊατρική τα τελευταία χρόνια αναπτύσσεται ραγδαία. Η άσκηση και η παροχή ιατρικών υπηρεσιών από απόσταση με τη χρήση της πληροφορικής και των τηλεπικοινωνιακών τεχνολογιών μπορεί να αποβεί σωτήρια για την ανθρώπινη ζωή, κυρίως σε περιπτώσεις απομονωμένων περιοχών (π.χ. νησιά, ορεινά χωριά), όπου η πρόσβαση σε ιατρικές υπηρεσίες είναι χρονοβόρα ή ακόμη και ανέφικτη. Ισχυρές γλώσσες προγραμματισμού, όπως η JavaME, οι οποίες υποστηρίζονται από τα σύγχρονα κινητά τηλέφωνα, δίδουν τη δυνατότητα σχεδιασμού και ανάπτυξης χρήσιμων εφαρμογών Τηλεϊατρικής. Στην παρούσα εργασία θα παρουσιαστεί η τεχνογνωσία που απαιτείται προκειμένου να μετατραπεί το κινητό τηλέφωνο σε μια συσκευή καταγραφής, επεξεργασίας, απεικόνισης και αποστολής του ηλεκτροκαρδιογραφικού σήματος. Tο ηλεκτροκαρδιογραφικό σήμα μετατρέπεται σε ηχητικό, μέσω της διαμόρφωσης εύρους, προκειμένου να αναγνωρισθεί από το κινητό τηλέφωνο και έπειτα μετατρέπεται πάλι σε ηλεκτροκαρδιογραφικό, μέσω της αποδιαμόρφωσης, προκειμένου να απεικονισθεί και να επεξεργαστεί περαιτέρω από το κινητό τηλέφωνο. Τέλος, θα παρουσιαστεί το αντίστοιχο λογισμικό που κατασκευάστηκε ώστε να επιτελεσθούν οι παραπάνω λειτουργίες. / Telemedicine is being rapidly developed. Practice and provision of medical services from distance via the use of information and telecommunication technology can prove lifesaving, especially in cases of isolated places (i.e. islands, mountainous villages), where access to medical services is time-consuming or infeasible. Powerful programming languages, such as JavaME, which are supported by the modern mobile phones, gives the possibility for designing and developing of useful telemedicine applications. In this thesis, it will be presented the know-how that is required in order to transform the mobile phone into a device able to capture, process, display and transmit the electrocardiographic signal. The electrocardiographic signal is transformed into a sound signal, through modulation, with a view to be recognizable by the mobile phone and then it is transformed again into an electrocardiographic signal, through demodulation, with a view to be further processed by the mobile phone. Also, it will be presented the software that has built so as to carry out these functions.