Gustavo Lenis

Electrocardiographic features for the measurement of drivers' mental workload

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.

P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference.

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.

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

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 (Δα), 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 Δα, 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 Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 μV, 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 Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα 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 Δα 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 Δα, TS and IAA.

Characterization of Radiofrequency Ablation Lesion Development Based on Simulated and Measured Intracardiac Electrograms

Radiofrequency ablation (RFA) therapy is the gold standard in interventional treatment of many cardiac arrhythmias. A major obstacle is nontransmural 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 EGMs reproduced the clinical changes, reaching up to 83% negative peak amplitude reduction and 80% decrease in maximum negative deflection for transmural lesions. In future studies, the established model may enable the development of further EGM criteria for transmural lesions even for complex geometries in order to support clinical therapy.

Ectopic beats and their influence on the morphology of subsequent waves in the electrocardiogram.

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 vector 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.

Removing ventricular far-field signals in intracardiac electrograms during stable atrial tachycardia using the periodic component analysis

BACKGROUND Intracardiac electrograms are an indispensable part during diagnosis of supraventricular arrhythmias, but atrial activity (AA) can be obscured by ventricular far-fields (VFF). Concepts based on statistical independence like principal component analysis (PCA) cannot be applied for VFF removal during atrial tachycardia with stable conduction. METHODS A database of realistic electrograms containing AA and VFF was generated. Both PCA and the new technique periodic component analysis (πCA) were implemented, benchmarked, and applied to clinical 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 of PCA failed during temporal coupling (cc=0.03±0.08) but improved for increasing conduction variability (cc=0.77±0.14). Stability of ACL was identified as a critical parameter for πCA application. 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 demonstrated on measured electrograms.

Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study

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.

Simulation of intracardiac electrograms around acute ablation lesions

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. Intracardiac electrograms (IEGMs) provide real-time information regarding the state of the cardiac tissue surrounding the catheter tip. Nevertheless, the formation and interpretation 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 phenomena 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 compared to an existing one and to clinical measurements of five 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 orthogonal catheter orientation, the presence of a second necrotic core in the near-field 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.

Atrial Signals – Modeling Meets Biosignal Analysis

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.

Orthogonal component analysis to remove ventricular far field in non periodic sustained atrial flutter

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.