Abstract:

Ventricular ectopic beats (VEBs) trigger a characteristic response of the heart called heart rate turbulence (HRT). The HRT can be used to predict sudden cardiac death in patients with a history of myocardial infarction. In this work, we present a reliable algorithm to detect and classify ectopic beats. Every electrocardiogram (ECG) is processed with innovative filtering techniques, artifact detection methods, and a robust multichannel analysis to produce accurate annotation results. For the classification task, a support vec- tor machine was used. Furthermore, a new approach to the analysis of HRT is proposed. The HRT is interpreted as the response of a second-order system to an external perturbation. The system theoretical parameters were estimated. The influence of VEB on the morphology of subsequent T waves was also analyzed. A strong influence was detected in the study with 14 patients experiencing frequent VEB. The evolution of the morphology of the T wave with every new beat was studied, and it could be concluded that an exponential shape underlies this dynamic process and was called morphological heart rate turbulence (MHRT). Parameters were defined to quantify the MHRT. The analysis of the MHRT could help to understand the influence of an ectopic beat on the repolarization processes of the heart and more accurately stratify the risk of sudden cardiac death.

Abstract:

Heart Rate Turbulence (HRT) is the distinctive response of the sinus rhythm of the heart to an isolated ventricular ectopic beat (VEB). The quantification of this process can be used to stratify the risk of sudden cardiac death in patients with a history of acute myocardial infarction. A sensitivity of around 30% has been achieved in different studies. However, the large number of misleading results of the method suggests that new and better risk stratifiers could be developed. In this work, Holter ECG recordings were used to analyze the morphology of the T wave during the HRT in patients with chronic heart failure. The HRT was characterized by newly introduced parameters. In ad- dition, the comparison between normal T waves before and after the VEB showed small but significant changes in mor- phology. The morphological changes of the T wave could be used for diagnostic purposes.

Abstract:

Dynamic modeling of physiological time series represents an auspicious approach in the arena of biomedical signal processing. This study illustrates a new methodology for identifying dynamic models that is based on stationary stochastic processes. The method is applied to time series extracted from the ECG. Simulations of the gained models yield physiologically plausible results.

Abstract:

The correct detection of the P wave in the electrocardiogram (ECG) is very important for the evaluation of the atrial activity. The presented algorithm fusions intracardiac measurements and ECG data to detect P waves in the ECG. With this, it is possible to detect P waves simultaneously appearing with T waves and multiple P waves between two ventricular excitations.Die korrekte Erkennung der P-Welle im Elektrokardiogramm (EKG) ist äußerst wichtig zur Erkennung von Krankheiten in den Vorhöfen des Herzens. Hier soll ein Algorithmus vorgestellt werden, der die Informationen aus einer EKG-Messung und einer intrakardialen Messung der elektrischen Aktivität in den Vorhöfen kombiniert. Damit ist es möglich sowohl von T-Wellen überdeckte P-Wellen als auch mehrere P-Wellen zwischen zwei Kammeraktivierungen zu detektieren.