Abstract:

The electrocardiogram (ECG) is a standard cost-efficient and non-invasive tool for the early detection of various cardiac diseases. Quantifying different timing and amplitude features of and in between the single ECG waveforms can reveal important information about the underlying (dys-)function of the heart. Determining these features requires the detection of fiducial points that mark the on- and offset as well as the peak of each ECG waveform (P wave, QRS complex, T wave). Manually setting these points is time-consuming and requires a physician’s expert knowledge. Therefore, the highly modular ECGdeli toolbox for MATLAB was developed, which is capable of filtering clinically recorded 12-lead ECG signals and detecting the fiducial points, also called delineation. It is one of the few open toolboxes offering ECG delineation for P waves, T Waves and QRS complexes. The algorithms provided were evaluated with the QT database, an ECG database comprising 105 signals with fiducial points annotated by clinicians. The median difference between the fiducial points set by the boundary detection algorithm and the clinical annotations serving as a ground truth is less than 4 samples (16 ms) for the P wave and the QRS complex markers.

Abstract:

Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace’s equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.

Abstract:

The electrocardiogram (ECG) is the standard measurement device of the electrical heart activity. It is highly available and allows for a quick, inexpensive, and non-invasive monitor- ing. This is especially important for the diagnosis of cardiovascular disease (CVD) which is one of the major concerns for the health care system in Europe. CVD causes costs of C210 billion and is responsible for 3.9 million deaths (45% of all deaths) a year. Apart from risk factors, chronic kidney disease (CKD) and structural changes in the heart tissue are underlying pathologies causing CVD. Both diseases can lead to life-threatening arrhythmia. This is why the following two pathologies connected to CVD are focused on in this thesis: Electrolyte imbalances in CKD patients and ectopic foci in the ventricles autonomously triggering an excitation. In both cases, the overall goal is to develop methods with the help of simulated signals supporting diagnosis. In the first project, ECG simulations are used to optimize a signal processing workflow for an ECG-based estimation of blood potassium concentration ([K+]b) and blood calcium concentration ([Ca2+]b). The findings from the simulation studies are incorporated into two [K+]b estimation methods which are evaluated on patient data. Mean absolute estimation errors were 0.37 mmol/l for a patient-specific approach and 0.48 mmol/l for a global approach with patient-specific adjustment. Advantages compared to existing approaches are extensively discussed. All algorithms being important for a signal processing workflow are published under an open source license. The second project aims at estimating the location of ectopic foci with the surface ECG without knowing the individual geometry of the patient. 1,766,406 simulated ECG signals (body surface potential maps (BSPMs)) are utilized to train two convolutional neural net- works (CNNs): The first estimates start and end of the depolarization, the second uses the depolarization part in the BSPM to localize the excitation origin. This CNN is designed to be able to show multiple solutions in the case of several possible excitation origins. The smallest median localization errors were 1.54 mm on the test set for the simulated and 37 mm for the patient data. Hence, the combination of the two CNNs yields a reliable method for the localization of ectopic foci on simulated and on patient data, although patient signals were not used during training. The results from the two projects demonstrate how simulated data can be used to develop and improve adequate ECG signal processing methods and how diagnosis can be supported. Furthermore, the potential of the combination of simulations and CNNs for overcoming the problem of unavailable clinical datasets as well as for finding estimation models being valid for different patients is demonstrated. The proposed methods can be used to accelerate diagnosis and is therefore likely to improve the outcome of the patients.