Objectives: The inverse relationship between QT interval variability (QTV) and T wave amplitude potentially confounds QT variability assessment. We quantified the influence of the T wave amplitude on QTV in a comprehensive dataset and devised a correction formula. Methods: Three ECG datasets of healthy subjects were analyzed to model the relationship between T wave amplitude and QTV. To derive a generally valid correction formula, linear regression analysis was used. The proposed correction formula was applied to patients enrolled in the Evaluation of Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation trial (DEFINITE) to assess the prognostic significance of QTV for all-cause mortality in patients with non-ischemic dilated cardiomyopathy. Results: A strong inverse relationship between T wave amplitude and QTV was demonstrated, both in healthy subjects (R2 = 0.68, p < 0.001) and DEFINITE patients (R2 = 0.20, p < 0.001). Applying the T wave amplitude correction to QTV achieved 2.5-times better group discrimination between patients enrolled in the DEFINITE study and healthy subjects. Kaplan-Meier estimator analysis showed that T wave amplitude corrected QTVi is inversely related to survival (p < 0.01) and a significant predictor of all-cause mortality. Conclusion: We have proposed a simple correction formula for improved QTV assessment. Using this correction, predictive value of QTV for all-cause mortality in patients with non-ischemic cardiomyopathy has been demonstrated.

This work deals with ECG signal measuring and methods of its processing. It compares some of the QRS detection methods and describes some of the testing databases. In this work a detector of QRS complex is realized, it is based on the approach of zero crossings. Next section makes combination of results from separate leads to one, which improves efficiency of detection. One section of this work deals with design and realization delination of ECG signal. In the last part outputs of this delineation are compared with the results of the other authors.

Les récentes avancées dans le domaine de la miniaturisation des capteurs biomédicaux à ultra-faible consommation énergétique, permettent aujourd’hui la conception de systèmes de télésurveillance médicale, à la fois plus intelligents et moins invasifs. Ces capteurs sont capables de collecter des signaux vitaux tels que le rythme cardiaq ue, la température, la saturation en oxygène, la pression artérielle, l'ECG, l'EMG, etc., et de les transmettre sans fil à un smartphone ou un autre dispositif distant. Ces avancées sus-citées ont conduit une large communauté scientifique à s'intéresser à la conception de nouveaux systèmes d'analyse de données biomédicales, en particulier de l’électrocardiogramme (ECG). S’inscrivant dans cette thématique de recherche, la présente thèse s’intéresse principalement à l’analyse et à la détection automatique des maladies cardiaques coronariennes, en particulier l’ischémie myocardique et l’infarctus du myocarde (IDM). A cette fin, et compte tenu de la nature non stationnaire et fortement bruitée du signal ECG, le premier défi a été d'extraire les paramètres pertinents de l’ECG, sans altérer leurs caractéristiques essentielles. Cette problématique a déjà fait l’objet de plusieurs travaux et ne représente pas l’objectif principal de cette thèse. Néanmoins, étant un prérequis incontournable, elle a nécessité une étude et une compréhension de l'état de l'art afin de sélectionner la méthode la plus appropriée. En s'appuyant sur les paramètres ECG extraits, en particulier les paramètres relatifs au segment ST et à l'onde T, nous avons contribué dans cette thèse par deux approches d'analyse ECG : (1) Une première analyse réalisée au niveau de la série temporelle des paramètres ECG, son objectif est de détecter les élévations anormales du segment ST et de l'onde T, connues pour être un signe précoce d'une ischémie myocardique ou d’un IDM. (2) Une deuxième analyse réalisée au niveau des battements de l’ECG, dont l’objectif est la classification des anomalies du segment ST et de l’onde T en différentes catégories. Cette dernière approche est la plus utilisée dans la littérature, cependant, il est difficile d’interpréter les résultats des travaux existants en raison de l'absence d’une méthodologie standard de classification. Nous avons donc réalisé notre propre étude comparative des principales méthodes de classification utilisées dans la littérature, en prenant en compte diverses classes d'anomalies ST et T, plusieurs paramètres d'évaluation des performances ainsi que plusieurs dérivations du signal ECG. Afin d'aboutir à des résultats plus significatifs, nous avons également réalisé la même étude en prenant en compte la présence d'autres anomalies cardiaques fréquentes dans l’ECG (arythmies). Enfin, en nous basant sur les résultats de cette étude comparative, nous avons proposé une nouvelle approche de classification des anomalies ST-T en utilisant une combinaison de la technique du Boosting et du sous-échantillonnage aléatoire, notre objectif étant de trouver le meilleur compromis entre vrais-positifs et faux-positifs. / Recent advances in sensing and miniaturization of ultra-low power devices allow for more intelligent and wearable health monitoring sensor-based systems. The sensors are capable of collecting vital signs, such as heart rate, temperature, oxygen saturation, blood pressure, ECG, EMG, etc., and communicate wirelessly the collected data to a remote device and/or smartphone. Nowadays, these aforementioned advances have led a large research community to have interest in the design and development of new biomedical data analysis systems, particularly electrocardiogram (ECG) analysis systems. Aimed at contributing to this broad research area, we have mainly focused in this thesis on the automatic analysis and detection of coronary heart diseases, such as Ischemia and Myocardial Infarction (MI), that are well known to be the leading death causes worldwide. Toward this end, and because the ECG signals are deemed to be very noisy and not stationary, our challenge was first to extract the relevant parameters without losing their main features. This particular issue has been widely addressed in the literature and does not represent the main purpose of this thesis. However, as it is a prerequisite, it required us to understand the state of the art proposed methods and select the most suitable one for our work. Based on the ECG parameters extracted, particularly the ST segment and the T wave parameters, we have contributed with two different approaches to analyze the ECG records: (1) the first analysis is performed in the time series level, in order to detect abnormal elevations of the ST segment and the T wave, known to be an accurate predictor of ischemia or MI; (2) the second analysis is performed at the ECG beat level to automatically classify the ST segment and T wave anomalies within different categories. This latter approach is the most commonly used in the literature. However, lacking a performance comparison standard in the state of the art existing works, we have carried out our own comparison of the actual classification methods by taking into account diverse ST and T anomaly classes, several performance evaluation parameters, as well as several ECG signal leads. To obtain more realistic performances, we have also performed the same study in the presence of other frequent cardiac anomalies, such as arrhythmia. Based on this substantial comparative study, we have proposed a new classification approach of seven ST-T anomaly classes, by using a hybrid of the boosting and the random under sampling methods, our goal was ultimately to reach the best tradeoff between true-positives and false-positives.

Elektrokardiografi är en vanligt förekommande undersökning inom hälso- och sjukvården då undersökningen är icke-invasiv och ger en bra överblick över hjärtats elektrofysiologiska status. Ett vanligt fel vid EKG är elektrodplacering i andra respektive tredje intercostala utrymmet istället för i fjärde intercostala utrymmet (IC4), vilket bland annat kan medföra reducering av R-vågs amplituden samt påverka datortolkningen. Syftet med arbetet var att se om avvikande EKG-kurvor uppstår vid omplacering av elektroderna V1 och V2 i andra och tredje intercostala utrymmet vid jämförelse med fjärde intercostala utrymmet på friska vuxna. I studien ingår 50 deltagare i åldern 18-48. Den statistiska analysen utfördes med hjälp av parvis T-test samt Fisher exact test. Jämförelsen mellan de olika placeringarna resulterade i en signifikant minskning av R-vågsamplituden (p &lt;0,001) för samtliga kombinationer. En signifikant minskning visades i R-vågsduration i jämförelsen Intercostalrum 2 (IC2) versus (vs) IC4 i V2. För ST-sträckan visade båda avledningarna för jämförelsen IC2 vs IC4 en signifikant skillnad (p&lt;0,001). Datortolkningen visade en signifikant skillnad för IC2 vs IC4 (p0,006) samt IC3 vs IC4 (p&lt;0,001).  Omplacering av elektroder medför förändrad datortolkning när det vid en standardplacering visar ett normalt EKG, vilket kan ge en inverkan på patientens fortsatta handläggning och eventuella diagnos. / Electrocardiography is commonly used in healthcare as the examination is non-invasive and provides a good overview of the heart's electrophysiological status. A common error in ECG positioning is placement in the second or third intercostal space, which among other things can lead to a reduction in R wave amplitude and affect the computer interpretation. The aim of the study is to see if deviant ECG curves occur when repositioning the electrodes V1 and V2 in the second and third intercostal space when compared to the fourth intercostal space. The study includes 50 healthy adults aged 18-48. The statistical analysis was performed using paired t tests and Fisher exact test. The comparison resulted in a significant reduction in R wave amplitude (p &lt;0.001) for all combinations. A significant decrease was shown in R wave duration in the comparison of IC2 vs IC4 in V2. For the ST segment, both leads for the comparison IC4 vs IC2 a significant difference was presented (p &lt;0.001). For the computer interpretation, a significant difference was shown for IC2 vs IC4 (p0.006) and IC3 vs IC4 (p&lt;0.001). Repositioning of electrodes leads to a different computer interpretation when in standard positioning shows a normal ECG, which may lead to misdiagnosis.

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The thesis deals with detection of T-wave alternans. The presence of T-wave in surface ECG is recognized as a marker of electrical instability of heart in stage his repolarization, arise increased risk of emergence ventricular fibrillation and sudden cardiac death. The goal of our project is familiarize with methods of detection T-wave alternans. In particular spectral method and spectral method which was realized in variant for running reading values in time (“sliding window”). To suggest a QRS complex detector, localize the T-wave and to make TWA detection using spectral method and modified spectral method. This project is to be made in C language in appropriate user interface.

This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.