Abstract:

Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32+/-12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.

Abstract:

Cardiologists measure electric signals inside the human heart aiming at a better diagnosis and optimized therapy of atrial arrhythmias like atrial flutter and atrial fibrillation. The catheters that are used for this purpose are improving: now they are able to pick up the electric signals at up to 64 positions inside the heart simultaneously. The patterns of electric depolarization are sometimes very simple, comparable to plane waves. But in case of patients with severe atrial arrhythmias they can be quite complex: U-turns around a line of block, ectopic centres, break throughs, reentry circuits, rotors, fractionated signals and chaotic patterns are often observed. Methods of biosignal analysis can support the cardiologists in classifying the signals and extract information of high diagnostic relevance. Computer models of the electrophysiology of the human heart can serve to design better algorithms for data analysis and to test algorithms, because the ground truth is known.

Abstract:

The post extrasystolic T wave change (PEST) is an electrocardiographic phenomenon in which the morphology of the normal T wave is altered for a short time after a ventricular ectopic beat (VEB). It has been observed in patients with other cardiac pathologies but it has not been proposed as a risk index for cardiac death. Since PEST seems to be potentiated in patients with depression of myocardial contractility, we hypothesize that PEST could be used to predict pump failure death (PFD) in patients with chronic heart failure (CHF). For the purpose of quantifying PEST, the parameters morphological change onset (MCO) and morphological change slope (MCS) were introduced. The MUSIC study was used to test the hypothesis. The patients in the study were separated according to its cause of death and comparisons of each cause against the others (including survivors) were carried out. In addition, the parameters MCO and MCS were divided into subgroups us- ing optimal values obtained from the corresponding ROC curves with the aim of analyzing predictability with respect to PFD. The results showed that no significant differences could be established and the proposed parameters do not seem to be related to any kind of cardiac death.

Abstract:

Microsleep events (MSE) are short intrusions of sleep under the demand of sustained attention. They can impose a major threat to safety while driving a car and are considered one of the most significant causes of traffic accidents. Drivers fatigue and MSE account for up to 20% of all car crashes in Europe and at least 100,000 accidents in the US every year. Unfortunately, there is not a standardized test developed to quantify the degree of vigilance of a driver. To account for this problem, different approaches based on biosignal analysis have been studied in the past. In this paper, we investigate an electrocardiographic-based detection of MSE using morphological and rhythmical features. 14 records from a car driving simulation study with a high incidence of MSE were analyzed and the behavior of the ECG features before and after an MSE in relation to reference baseline values (without drowsiness) were investigated. The results show that MSE cannot be detected (or predicted) using only the ECG. However, in the presence of MSE, the rhythmical and morphological features were observed to be significantly different than the ones calculated for the reference signal without sleepiness. In particular, when MSE were present, the heart rate diminished while the heart rate variability increased. Time distances between P wave and R peak, and R peak and T wave and their dispersion increased also. This demonstrates a noticeable change of the autonomous regulation of the heart. In future, the ECG parameter could be used as a surrogate measure of fatigue.

Abstract:

Computer simulations and imaging of human physiology and anatomy are effectively used for diagnostics and medical treatments and are thus a focus of scientific research. Suitable representation of data is a critical aspect to achieve best results. Therefore, we developed an interactive visualization scheme especially for the representation of cardiac arrhythmias based on a conventional mobile device and virtual reality (VR) goggles (Google Cardboard and Samsung Gear VR) in combination with a game engine. The aim of this paper is to raise awareness for this new technique, evaluate its potential and pro- pose a general workflow for such a visualization environment. The use of a conventional mobile device in combination with VR goggles creates a portable and low-cost system, equipped with enough processing power and pixel density for many types of applications. The user can interact with the data through head movement or a secondary controller. As current game engines support a wide range of additional input methods and controllers, the interaction method can be customized to fit the target audience. To evaluate this method, we conducted a survey with eight typical phenomena from the field of cardiac arrhythmias. The participants were asked to rate different performance aspects on a scale from one (very bad) to five (very good). All participants (N=27) rated the performance as fluent (median=5). Furthermore, most participants (70%) ranked the overall impression as very good (median=5). On the long run, the system can be used for education and presentations as well as improved planning and guidance of medical procedures.

Abstract:

Radiofrequency ablation (RFA) is a widely used clinical treatment for many types of cardiac arrhythmias. However, nontransmural lesions and gaps between linear lesions often lead to recurrence of the arrhythmia. Intrac- ardiac electrograms (IEGMs) provide real-time informa- tion regarding the state of the cardiac tissue surrounding the catheter tip. Nevertheless, the formation and inter- pretation of IEGMs during the RFA procedure is complex and yet not fully understood. In this in-silico study, we propose a computational model for acute ablation lesions. Our model consists of a necrotic scar core and a border zone, describing irreversible and reversible temperature induced electrophysiological phenomena. These phenom- ena are modeled by varying the intra- and extracellular conductivity of the tissue as well as a regulating zone factor. The computational model is evaluated regarding its feasibility and validity. Therefore, this model was com- pared to an existing one and to clinical measurements of ve patients undergoing RFA. The results show that the model can indeed be used to recreate IEGMs. We computed IEGMs arising from complex ablation scars, such as scars with gaps or two overlapping ellipsoid scars. For orthogo- nal catheter orientation, the presence of a second necrotic core in the near- eld of a punctiform acute ablation lesion had minor impact on the resulting signal morphology. The presented model can serve as a base for further research on the formation and interpretation of IEGMs.

Abstract:

Catheter ablation has become a very efficient strategy to terminate sustained cardiac arrhythmias like atrial flutter (AFlut). Identification of the optimal ablation spot, however, often proves difficult when scar from previous ablations is present. Although the application of electro-anatomical mapping systems allows to record thousands of intracardiac electrograms (EGMs) from each atrium, state-of-the-art techniques provide limited options for automatic signal processing. Goal of the presented research was the development of an algorithm to detect EGMs that present double potentials (DPs), as these often indicate functional or anatomical lines of block for cardiac excitation. Using an annotated database, we developed several features based on the morphological descriptors of DPs. These were used to train a binary decision tree which was able to detect DPs with a correct rate of over 90%.

Abstract:

This work investigates the impact of time constant offset in the body surface potential map (BSPM) on the recon- struction quality in electrocardiographic imaging (ECGI). For this purpose, a study comparing four different approaches for the reconstruction of the transmembrane voltage distribution (TMV) was carried out. From this four methods two of them were newly designed to estimate and remove the offset from the BSPM. The first approach uses a new formulation of the Tikhonov-Greensite method as augmented regularization to estimate and remove the time constant offset during the reconstruction. The second algorithm is related to classical signal processing. It applies a mode filter to remove the time constant offset in the BSPM and afterwards reconstructs the ventricular ectopic beat (VEB) using the Tikhonov-Greensite regularization. It can be shown that the time constant offset has a significant influence on the reconstruction quality and should be removed. The preferred method to remove time constant offset is the mode filter.