Abstract:

This study examines the effect of mental workload on the electrocardiogram (ECG) of participants driving the Lane Change Task (LCT). Different levels of mental workload were induced by a secondary task (n-back task) with three levels of difficulty. Subjective data showed a significant increase of the experienced workload over all three levels. An exploratory approach was chosen to extract a large number of rhythmical and morphological features from the ECG signal thereby identifying those which differentiated best between the levels of mental workload. No single rhythmical or morphological feature was able to differentiate between all three levels. A group of parameters were extracted which were at least able to discriminate between two levels. For future research, a combination of features is recommended to achieve best diagnosticity for different levels of mental workload.

Abstract:

Heart rate variability (HRV) plays an important role in medicine and psychology because it is used to quantify imbalances of the autonomic nervous system (ANS). An important manifestations of the ANS on HRV is also directly related to respiration and it is called respiratory sinus arrhythmia (RSA). This is a controlled phenomenon that leads to a synchronized coupling between respiration and instantaneous heart rate. Thus, the portion of HRV that is not related to respiration, and could potentially contain undiscovered diagnostic value, is overlapped and remains hidden in a standard HRV analysis. In such cases, a decoupling procedure would deliver a discriminated HRV analysis and possible new insights about the regulation of the cardiovascular system. In this work, we propose an algorithm based on Granger's causality to measure coupling between respiration and HRV. In the case of significant coupling, we estimate and cancel the respiration driven HRV component using a linear filtering approach. We tested the method using synthetic signals and prove it to deliver a reliable coupling measurement in 96.3% of the cases and reconstruct respiration free signals with a median correlation coefficient of 0.992. Afterwards, we applied our method to signals recorded during paced respiration and during natural breathing. We demonstrated that coupling is dependent on respiratory frequency and that it maximizes at 0.3 Hz. Furthermore, the HRV parameters measured during paced respiration tend to level among subjects after decoupling. The intersubject variability of HRV parameter is also decreased after the separation process. During natural breathing, coupling is notoriously lower to non-existing and decoupling has little impact on HRV. We conclude that the method proposed here can be used to investigate the diagnostic value of respiration independent HRV parameters.

Abstract:

The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.

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:

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:

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:

OBJECTIVE: Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition. METHODS: We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data: Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data. RESULTS: VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029mV, yielding a median VFF attenuation of 142. In a different setup, only VFF data acquired at distances of more than 5mm to the atrial endocardium were used to estimate the model parameters. The VFF component was then extrapolated for a layer of 5mm thickness lining the endocardial tissue. A median deviation of 0.082mV (median VFF attenuation of 49x) was achieved under realistic noise conditions. CONCLUSION: It is feasible to model the VFF component in a personalized way and effectively remove it from uEGMs. SIGNIFICANCE: Application of our novel, simple and computationally inexpensive methods allows immediate diagnostic assessment of uEGM data without prolonging data acquisition.

Abstract:

Optical mapping is widely used as a tool to investigate cardiac electrophysiology in ex vivo preparations. Digital filtering of fluorescence-optical data is an important requirement for robust subsequent data analysis and still a challenge when processing data acquired from thin mammalian myocardium. Therefore, we propose and investigate the use of an adaptive spatio-temporal Gaussian filter for processing optical mapping signals from these kinds of tissue usually having low signal-to-noise ratio (SNR). We demonstrate how filtering parameters can be chosen automatically without additional user input. For systematic comparison of this filter with standard filtering methods from the literature, we generated synthetic signals representing optical recordings from atrial myocardium of a rat heart with varying SNR. Furthermore, all filter methods were applied to experimental data from an ex vivo setup. Our developed filter outperformed the other filter methods regarding local activation time detection at SNRs smaller than 3 dB which are typical noise ratios expected in these signals. At higher SNRs, the proposed filter performed slightly worse than the methods from literature. In conclusion, the proposed adaptive spatio-temporal Gaussian filter is an appropriate tool for investigating fluorescence-optical data with low SNR. The spatio-temporal filter parameters were automatically adapted in contrast to the other investigated filters.

Abstract:

OBJECTIVE: Atrial tachycardia (AT) still pose a major challenge in catheter ablation. Although state-of-the-art electroanatomical mapping systems allow to acquire several thousand intracardiac electrograms (EGMs), algorithms for diagnostic analysis are mainly limited to the amplitude of the signal (voltage map) and the local activation time~(LAT map). We applied spatio-temporal analysis of EGM activity to generate maps indicating reentries and diastolic potentials, thus identifying and localizing the driving mechanism of AT. METHODS: First, the time course of active surface area (ASA) is determined during one basic cycle length (BCL). The global cycle length coverage (gCLC) reflects the relative duration within one BCL for which activity was present in each individual atrium. A local cycle length coverage (lCLC) is computed for circular sub-areas with 20mm diameter. The simultaneous active surface area sASA is determined to indicate the spatial extent of depolarizing tissue. RESULTS: Combined analysis of these spatial scales allowed to correctly identify and localize the driving mechanism: gCLC values of 100% were indicative for atria harbouring a reentrant driver. lCLC could detect micro reentries within an area of 1.651.28cm in simulated data and differentiate them against focal sources. Mid-diastolic potentials, being potential targets for catheter ablation, were identified as the areas showing confined activity based on sASA values. CONCLUSION: The concept of spatio-temporal activity analysis proved successful and correctly indicated the tachycardia mechanism in 20 simulated AT scenarios and three clinical data sets. SIGNIFICANCE: Automatic interpretation of intracardiac mapping data could help to improve the treatment strategy in complex cases of AT.

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:

Background: Intracardiac electrograms are an indispensable part during diagnosis of supraventriculararrhythmias, but atrial activity (AA) can be obscured by ventricular far-fields (VFF). Concepts based onstatistical independence like principal component analysis (PCA) cannot be applied for VFF removalduring atrial tachycardia with stable conduction.Methods: A database of realistic electrograms containing AAand VFF was generated. Both PCA and thenew technique periodic component analysis (πCA) were implemented, benchmarked, and applied toclinical data.Results: The concept of πCA was successfully verified to retain compromised AA morphology,showing high correlation (cc = 0.98 ± 0.01) for stable atrial cycle length (ACL). Performance ofPCA failed during temporal coupling (cc = 0.03 ± 0.08) but improved for increasing conductionvariability (cc = 0.77 ± 0.14). Stability of ACL was identified as a critical parameter for πCAapplication. Analysis of clinical data confirmed these findings.Conclusion: πCA is introduced as a powerful new technique for artifact removal in periodic signals.Its concept and performance were benchmarked against PCA using simulated data and demonstratedon measured electrograms.

Abstract:

BACKGROUND: Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Deltaalpha), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD. METHODS: Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Deltaalpha, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. RESULTS: Deltaalpha and IAA, dichotomized at 0.035 (dimensionless) and 3.73 muV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Deltaalpha>/=0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Deltaalpha and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS </= 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Deltaalpha and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD. CONCLUSIONS: The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG-derived risk markers like Deltaalpha, TS and IAA.

Abstract:

Radiofrequency ablation (RFA) therapy is the gold standard in interventional treatment of many cardiac arrhythmias. A major obstacle are non transmural lesions, leading to recurrence of arrhythmias. Recent clinical studies have suggested intracardiac electrogram (EGM) criteria as a promising marker to evaluate lesion development. Seeking for a deeper understanding of underlying mechanisms, we established a simulation approach for acute RFA lesions. Ablation lesions were modeled by a passive necrotic core surrounded by a borderzone with properties of heated myocardium. Herein, conduction velocity and electrophysiological properties were altered. We simulated EGMs during RFA to study the relation between lesion formation and EGM changes using the bidomain model. Simulations were performed on a three dimensional setup including a geometrically detailed representation of the catheter with highly conductive electrodes. For validation, EGMs recorded during RFA procedures in five patients were analyzed and compared to simulation results. Clinical data showed major changes in the distal unipolar EGM. During RFA, the negative peak amplitude decreased up to 104% and maximum negative deflection was up to 88% smaller at the end of the ablation sequence. These changes mainly occurred in the first 10 s after ablation onset. Simulated unipolar EGM reproduced the clinical changes, reaching up to 83% negative peak amplitude reduction and 80% decrease in maximum negative deflection for transmural lesions. In future work, the established model may enable the development of further EGM criteria for transmural lesions even for complex geometries in order to support clinical therapy.

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:

ECG derived respiration (EDR) is a technique applied to estimate the respiration signal using only the electrocardiogram (ECG). Different approaches have been proposed in the past on how respiration could be gained from the ECG. However, in many applications only one of them is used while the others are not considered at all. In this paper, we propose a new algorithm for the optimal linear combination of different EDR methods in order to create a more accurate estimation. Using two well known databases, it was statistically shown that an optimally chosen fixed set of coefficients for the linear combination delivers a better estimation than each of the methods used solely.

Abstract:

Automatic signal processing of intracardiac electrograms plays a decisive role in the diagnosis and treatment of supraventricular arrhythmias. During sustained atrial flutter, a repetitive signal is measured in the atrium. However, the ventricular far field may overlap with the atrial activity and compromises the automatic signal processing tools during the intervention. Recently, a new method based on periodic component analysis was proposed as an artifact removal technique. The method works satisfactorily with highly periodic atrial activities but fails to reconstruct not regularly repeating signals .In order to account for that case, we developed a new method based on orthogonal component analysis to reconstruct the corrupted atrial electrocardiograms obscured by ventricular far field. We tested the method on synthetic signals and proved it to be successful. The reconstructed signals were of higher quality and the computation time was drastically shorter than the already existing periodic component analysis. We conclude that the new method can be used in realistic scenarios in the future.

Abstract:

For the purpose of accurate preclinical drug screening and particularly to evaluate the risk of undesired cardiac arrhyth- mias or drug induced toxicity, human embryonic stem cell derived cardiomyocytes clusters can be used. A novelty micrcocavity array screening platform has been developed to facilitate recordings of extracellular field potentials and de- tect QT prolongation and cardiotoxic effects. The measured signal is similar to a human ECG with a missing P wave. In order to automate the drug screening process and delineate the filed potential recordings a signal-analyzing algorithm has been developed.

Abstract:

The risk stratification of sudden cardiac death after my- ocardial infarction plays an important role in cardiology. It influences the treatment of a patient and the use of im- plantable devices. However, the majority of well known methods for stratifying risk still fail to predict sudden car- diac death with high accuracy. The heart rate turbulence delivers good results that could be complemented by study- ing ECG morphology. For this purpose, the post extrasys- tolic T wave change was studied in this work. 10 patients with structural healthy ventricles were paced in the right ventricular apex and the subsequent response of the heart was measured in the ECG. Complementary, computer sim- ulations of the human transmembrane voltages and poste- rior ECG reconstruction were also carried out. Morpho- logical changes in the post extrasystolic T wave and its restitution to the original shape were measurable in every patient of this study. The patients presented diminished or alternating postectopic T waves and prolongation of T wave duration. However, the simulation does not present significant T wave changes. Furthermore, the new mor- phological parameters do not seem to correlate with the standard HRT parameters.

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:

The analysis of the heart rate turbulence (HRT) can be used to evaluate the risk of sudden cardiac death. For that purpose ectopic beats have to be classified. A method for automatic ectopic beat detection and classification based on a Support-Vector-Machine (SVM) was developed before. In this work an analysis similar to the HRT was carried out on the morphological features of the QRS complex and the T wave of the ECG following a ventricular premature contractions (VPC). A study of 56 subjects, suffering from a various number of ventricular premature contractions was conducted.

Abstract:

Die Analyse der Herz Raten Turbulenz (HRT) dient der Vorhersage eines ploetzlichen Herztodes. Hierzu muessen die ektopen Schlaege im EKG ausgewertet werden. Zur automatischen Detektion und Klassifikation ektoper Schlaege wurde 2010 eine Methode entwickelt, welche auf Basis einer Support-Vector-Machine (SVM) die Schlaege klassifiziert. Artefakte im EKG-Signal fuehren nicht selten zur Fehlklassifizierung, da sie nicht vollstaendig von ektopen Schlaegen unterschieden werden koennen. Um die Genauigkeit die HRT Analyse zu verbessern, wurde ein Algorithmus zur automatischen Unterscheidung von Artefakten und ektopen Schlaegen entwickelt.

Abstract:

Ectopic beats are a common cause of cardiac arrhythmia. In order to automatically detect and classify ectopic beats in the ECG a new method based on a Support Vector Machine (SVM) was developed. The numerical patterns needed for this classification task were obtained from rhythmical and morphological characteristics of the QRS complexes. The SVM was trained and tested using the MIT-BIH Arrythmia Database and the MIT-BIH Supraventricular Arrythmia Database. A sensitivity of 92.46% was achieved.

Abstract:

Today, patients suffering from atrial arrhythmias like atrial flutter (AFlut) or atrial fibrillation (AFib) are examined in the EP-lab (electrophysiology lab) in order to understand and treat the disease. Multichannel catheters are advanced into the atria in order to measureelectric signals at manyintracardiacpositions simultaneously. Complementary to clinical learning,comprehension of the disease and therapeutic strategies can be improved with computer modeling of the heart. This way, hypotheses about initiation and perpetuation of the arrhythmia can be tested and ablation strategies can be assessed in-silico. Modeling and biosignal analysis can benefit from mutual fertilization. On the one hand, modeling can be improved and personalization can be achieved via high density mapping of the atria. On the other hand, new algorithms for the interpretation of multichannel electrograms can be developed and evaluated with synthetic signals from computer models of the atria. This article illustrates the synergetic potential by examples and highlights challenges to be addressed in the future.

Abstract:

Cardiac arrhythmias are a widely spread disease in industrialized countries. A common clinical treatment for this disease is radiofrequency ablation (RFA), in which high frequency alternating current creates a lesion on the myocardium. However, the formation of the lesion is not entirely understood. To obtain more information about ablation lesions (ALs) and their electrophysiological properties, we established an in-vitro setup to record electrical activity of rat myocardium. Electrical activity is measured by a circular shaped multielectrode array. This work was focused to gain more information by developing algorithms to process the measured electrical signals to collect different features, which may allow us to characterize an AL. First, pacing artefacts were detected and blanked. Subsequently, data were filtered. Afterwards, activations in atrial signals were detected using a non-linear energy operator (NLEO) and templates of these activations were generated. Finally, we determined different features on each activation in order to evaluate changes of unipolar as well as bipolar electrograms and considered these features before and after ablation. In conclusion, the majority of the signal features delivered significant differences between normal tissue and lesion. Among others, a reduction in peak to peak amplitude and a diminished spectral power in the band 0 to 100 Hz may be useful indicators for AL. These criteria should be verified in future studies with the aim of estimating indirectly the formation of a lesion.

Abstract:

We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (1932 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-nets QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.

Abstract:

Atrial arrhythmias such as atrial flutter and atrial fibrillation are a burden for patients and a major challenge for modern healthcare systems. Identification of patients at risk to develop atrial arrhythmias at an early stage carries the potential to reduce the incidence by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the intiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG lead positions to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave amplitude was identified at the center of the back consistently across five models of the cohort. Optimized linear combinations of standard 12-lead signals yielded comparably good results. Our newly proposed electrode positions on the back as well as selected linear combinations of standard 12-lead signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding the anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Cardiac excitation during atrial fibrillation (AFib) is changing dynamically, compromising the ability to identify underlying mechanisms by intracardiac catheter mapping. Statistical analysis of dominant excitation patterns may help to identify and subsequently eliminate the drivers of this tachycardia. As the morphology of local bipolar intracardiac electrograms (EGMs) depends on the orientation of the propagating excitation wave, its evaluation for a fixed multichannel catheter position can provide information about the stability of the depolarization pattern. Up to date, analysis of morphology is most often done by computing a similarity index or the recurrence rate of individual EGMs, reflecting how often similar excitations appear. We sougth to extend this approach to a classification based analysis technique. In each multichannel EGM, local activation waves (LAWs) were automatically detected by assessing instantaneous signal energy. A greedy algorithm was implemented to cluster LAWs based on their similiarity. New clusteres were formed when similarity fell below a predefined threshold. The concept was tested using simulated EGM data (quadratic patch of cardiac tissue, bidomain simulation, both planar and focal excitations, various catheter types). Results demonstrated that the algorithm correctly identified and classified the simulated excitation patterns. Subsequent quantitative analysis allowed to both discard singular classes of excitation and identify dominant excitations. The presented method forms the basis for statistical assessment of prevailing depolarization patterns, and for computation of additional features like conduction velocity, presence of focal sources, or dissociation when applied on multichannel data.

Abstract:

A common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

Abstract:

Intracardiac electrogram recordings during atrial fibrillation (AFib) are characterized by irregular rhythms and complex morphologies. Hence, analysis in the time domain is a difficult task. The so called dominant frequency DF is a spectrum based approach that aims at finding the most relevant frequency in a signal providing information about the rate and dynamics of AFib. However, in recent years various studies reported controversial results regarding the clinical relevance of the DF. In this work, a definition of the DF at a fundamental scale is proposed as the rate at which action potentials are triggered in atrial cells. The most common method to estimate the DF in literature, labeled as DFSpec, is examined in comparison to the proposed definition. A signal processing study using synthetic signals verified that the DFSpec is stable for all changes in morphology of atrial activations. However, it is also demonstrated that the DFSpec becomes unstable for variations above 20% in the cycle length of a signal. Spectrum based DF estimation should be interpreted in a critical manner and is not advisable for study endpoints or clinical markers.

Abstract:

The heart rate variability (HRV) is a measure which is commonly used to assess sympathetic and parasympathetic auto-nomic function. It is well known, that respiration can have a strong influence on HRV. Especially, a phenomenon called Respiratory Sinus Arrythmia (RSA) modulates the RR intervals and is a major contributor to the HRV. The interpreta-tion of common HRV parameters can be ambiguous due to different respiration rates and patterns. To assess this ambi-guity, the coupling of RSA on HRV was quantified and the HRV parameters were compared during different respirato-ry states.A pilot study with five healthy subjects was performed. A three lead ECG was acquired and the respiration was estimat-ed by measuring the aeration of the lungs using the PulmoVista 500 by Dräger. This device uses Electrical Impedance Tomography (EIT) to monitor impedance changes due to the changing amount of air within the lungs during respira-tion. The subjects were asked to breath at controlled respiration rates of 8, 15 and 24 breaths per minute as well as spon-taneously for 1 min each. In addition, to analyze HRV during apnoic phases without any respiration, the subjects were asked to hold their breath for 40s at end-inspiration and end-expiration. After preprocessing of the ECG and the respiration signal, the coupling between the measured respiration and the RR intervals was quantified using the Granger causality. If significant coupling was present, the HRV was separated from its respiratory influence using an ARMAX model. The measured respiration hereby formed the exogeneous input to the filter. Finally, common HRV parameters were calculated for the original and the decoupled RR intervals.We showed, that coupling strength depends on respiratory rates, which might complicate HRV interpretation. Moreo-ver, the coupling is decreased during spontaneous breathing in comparison to controlled respiration. Additionally we found, that HRV parameters during apnoic phases differ from decoupled HRV parameters during spontaneous or con-trolled respiration.

Abstract:

Atrial fibrillation and atrial flutter are the most common atrial arrhythmias placing a heavy burden on patients and posing a challenge on healthcare systems. If patients at risk to develop atrial arrhythmias can be identified at an early stage, the arrhythmia incidence can be lowered by implementing appropriate strategies to mitigate the risks. Diagnostic methods based on the ECG are ideal risk markers due to their noninvasiveness and omnipresence. The left atrium (LA) plays a major role in the initiation and perpetuation of atrial reentry arrhythmias. However, the LA is not well represented in the P-wave derived through standard ECG leads. Here, we optimize ECG leads to maximize LA information content. Towards this end, we used a cohort of eight personalized computational models providing the unique opportunity to separate LA and right atrial (RA) contributions to the P-wave, which is not feasible in vivo. The location of maximum P-wave signal energy was located on the center of the chest for all subjects with marked overlap between regions of maximum LA and RA P-wave amplitude. The regions of highest ratio between LA and RA signal energy differed between patients. However, a region with LA signal energy being higher than that of the RA and providing a sufficiently large absolute P-wave signal energy was identified at the lower left quadrant of the back consistently across most subjects of the cohort. Optimized linear combinations of standard 12-lead signals (considering the eight independent leads) yielded comparably good results amplifying LA information by more than one order of magnitude. Our newly proposed electrode positions on the back as well as selected combinations of standard ECG signals improve the LA information content considerably. By using these, more relevant diagnostic information regarding anatomical and electrophysiological properties of the LA can be derived in future.

Abstract:

Chronic kidney disease appears worldwide. In the United States, the number of patients suffering from kid- ney failure doubled from 1998 to 2010. A common treat- ment for these patients is haemodialysis. However, the frequency of deaths caused by cardiovascular diseases is up to 10% to 30% higher in patients undergoing dialysis than in the general population. To analyse the underly- ing effects and for a possible risk prediction, a continuous monitoring of the ionic concentrations that are influenced by dialysis is desired. In this work, a method for the re- construction of the ionic concentrations of calcium and potassium from the ECG is proposed. In a first step, 91 monodomain simulations with the ten Tusscher ventricular cell model were performed for different extracellular ionic concentrations. From there, a standard 12-lead ECG was extracted. Calcium and potassium changes yielded ECGs clearly differing in amplitude and morphology. In a second step, the simulated ECG signals were used for reconstruc- ting the ionic concentrations directly from the ECG. Fea- tures were extracted from the signals designed to describe changes caused by varied ionic concentrations. The in- verse problem, i.e. coming back from the ECG features to the ionic concentrations was solved by regression with an artificial neural network. Results for potassium estimation yield an error of 0.00±0.28 mmol/l (mean±standard de- viation) calculated with 7-fold cross validation. The esti- mation error for calcium was 0.00±0.08 mmol/l. Although these results underline the suitability of the method, the used ECGs differed from the observed in a clinical envi- ronment. However, simulations allow an evaluation un- der controlled conditions of a particular effect that was intended to be investigated. As the application to clinical data is yet missing, this study can be seen as a proof of concept showing that an artificial neural network is capa- ble of exactly estimating potassium and calcium concen- trations from ECG features. 1. Introduction Haemodialysis therapy is a common treatment method for patients suffering from chronic kidney disease (CKD) in the terminal stage. The amount of people in the United States suffering from kidney failure increased from 320,000 in 1998 to 650,000 in 2010. The frequency of deaths caused by cardiovascular events within the dialysis patient group is up to 10% to 30% higher than in gene- ral population [1]. Patients suffering from end-stage CKD experience high variations of blood electrolyte concentra- tions. These can directly influence the functioning of the heart. Thus, research on cardiovascular links could im- prove therapy and risk stratification. One tool which is capable of capturing the electrophysiological properties of the heart in a non-invasive way is the electrocardiogram (ECG). It is known, that electrolyte concentrations of po- tassium (K+) and calcium (Ca2+) affect the ECG [2]. Un- til now, a determination of the concentrations is connec- ted to a blood test. Hence, continuous monitoring of the ionic concentration is impracticable. However, the ECG as a continuous, non-invasive monitoring tool could shed a light on the relation between heart diseases and changes in the ionic concentration particularly after leaving the strictly supervised clinical area where dialysis takes place, i.e allowing a monitoring at home. Articles have been pub- lished showing that the reconstruction of extracellular K+ concentration can be done using just one feature from the ECG with a quadratic regression [3]. In this study, we tried to estimate both K+ and Ca2+ concentrations from the ECG. Therefore, we examined simulated ECGs at dif- ferent concentration levels and designed features descri- bing the observed changes in the ECG. A subset of these was used in connection with a machine learning method to reconstruct the concentrations. 2. Methods 2.1. Simulations A total number of 91 computer simulations of the car- diac electrophysiology were performed at whole heart

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.

Abstract:

Heart Rate Variability studies are a known measure for the autonomous control of the heart rate. In special situations, its interpretation can be ambiguous, since the respiration has a major influence on the heart rate variability. For this reason it has often been proposed to measure Heart Rate Variability, while the subjects are breathing at a constant respiration rate. That way the spectral influence of the respiration is known. In this work we propose to remove this constant respiratory influence from the heart rate and the Heart Rate Variability parameters to gain respiration free autonomous controlled heart rate signal. The spectral respiratory component in the heart rate signal is detected and characterized. Subsequently the respiratory effect on Heart Rate Variability is removed using spectral filtering approaches, such as the Notch filter or the Raised Cosine filter. As a result new decoupled Heart Variability parameters are gained, which could lead to new additional interpretations of the autonomous control of the heart rate.

Abstract:

Baseline wander removal is one important part of electrocardiogram (ECG) filtering. This can be achieved by many different approaches. This work investigates the influence of three different baseline wander removal techniques on ST changes. The chosen filters were phase-free Butterworth filtering, median filtering and baseline correction with cubic spline interpolation. 289 simulated ECGs containing ischemia were used to determine the influence of these filtering processes on the ST segment. Synthetic baseline wander and offsets were added to the simulated signals. All methods proved to be good approaches by removing most of the baseline wander in all signals. Correlation coefficients between the original signals and the filtered signals were greater than 0.93 for all ECGs. Cubic spline interpolation performed best regarding the preservation of the ST segment amplitude change when compared to the original signal. The approach modified the ST segment by 0.10mV±0.06mV at elevated K points. Median filtering introduced a variation of 0.33mV±0.29mV, Butterworth filtering reached 0.16mV±0.14mV at elevated K points. Thus, all methods manipulate the ST segment.

Abstract:

Ectopic beats are a common cause for cardiac arrhythmia. The methods presented in this paper deal with the evaluation of the features that are used by an existing classifier to distinguish between normal, supraventricular ectopic and ventricular ectopic beats. In order to classify the beats, a support vector machine (SVM) is used. Since noisy features can confuse the classifier and downgrade its performance, high quality features should be chosen. In the end, the performance should be improved by using only the selected features after the evaluation process. For this purpose, a receiver operating character- istic (ROC) analysis was conducted first. Secondly, the Gini diversity index (GDI) was calculated for every feature which is often used as split criterion in decision trees. As a third evaluation tool, the information gain ratio (IGR) was applied to estimate the quality of the features. To conclude the evaluation part, a fourth analysis was implemented. The ROC was applied again to the beats that are falsely classified in a first run-through. This was a first step into a deeper investigation of the dependency among features. As result of the evaluation process, a feature ranking was built and 36 of the 55 features were chosen to build the new SVM. A training and testing process was conducted using beats of the MIT-BIH-Arrhythmia- Database. A correct rate of 98.574%, a sensitivity of 98.592% and a positive predictive value of 99.062% were achieved.

Abstract:

Post-ablation atrial flutter(AF) is a frequently occurring arrhythmia after treatment for persistent atrial fibrillation. However, mapping the flutter circuit using intracardiac electrograms is often challenging due to low signal voltage and scar areas caused by prior substrate modification. In addition, signals are frequently compromised by ventricular far field (VFF) artifacts, which obscure atrial activity (AA). This work introduces a new approach for VFF removal, which is based on the Periodic Component Analysis (&#1113099;CA). It utilizes the stable temporal relationship between AA and VFF, which poses a problem for other techniques like Principal Component Analysis (PCA) when both components superpose. A benchmark using simulated electrograms demonstrated significantly better correlation for this case when comparing pure AA to the reconstructed data using &#1113099;CA instead of PCA (0.98 vs. 0.90, p<0.001). Its benefit for diagnosis is demonstrated on clinical data.

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.

Abstract:

Various types of heart disease are associated with structural remodeling of cardiac cells. In this work, we present a software framework for automated analyses of structures and protein distributions involved in excitation-contraction coupling in cardiac muscle cells (myocytes). The software framework was designed for processing sets of three-dimensional image stacks, which were created by fluorescent labeling and scanning confocal microscopy of ventricular myocytes from a rabbit infarction model. Design of the software framework reflected the large data volume of image stacks and their large number by selection of efficient and automated methods of digital image processing. Specifically, we selected methods with small user interaction and automated parameter identification by analysis of image stacks. We applied the software framework to exemplary data yielding quantitative information on the arrangement of cell membrane (sarcolemma), the density of ryanodine receptor clusters and their distance to the sarcolemma. We suggest that the presented software framework can be used to automatically quantify various aspects of cellular remodeling, which will provide insights in basic mechanisms of heart diseases and their modeling using computational approaches. Further applications of the developed approaches include clinical cardiological diagnosis and therapy planning.