Poincaré mapping for detecting abnormal dynamics of cardiac repolarization

Order-indeterminant event-based maps for learning a beat

The process by which humans synchronize to a musical beat is believed to occur through error-correction where an individual's estimates of the period and phase of the beat time are iteratively adjusted to align with an external stimuli. Mathematically, error-correction can be described using a two-dimensional map where convergence to a fixed point corresponds to synchronizing to the beat. In this paper, we show how a neural system, called a beat generator, learns to adapt its oscillatory behavior through error-correction to synchronize to an external periodic signal. We construct a two-dimensional event-based map, which iteratively adjusts an internal parameter of the beat generator to speed up or slow down its oscillatory behavior to bring it into synchrony with the periodic stimulus. The map is novel in that the order of events defining the map are not a priori known. Instead, the type of error-correction adjustment made at each iterate of the map is determined by a sequence of expected events. The map possesses a rich repertoire of dynamics, including periodic solutions and chaotic orbits.

Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia

OBJECTIVE

This study concentrates on subtle electrocardiogram (ECG) spatiotemporal characteristics in the repolarization phase, and describes a deterministic learning-based methodology for the detection of abnormal cardiac dynamics induced by ischemia.

METHODS

ST-T complex of the surface 12-lead ECG signals are identified and extracted. Cardiac dynamics underlying ST-T complex signals is captured using deterministic learning algorithm. This kind of dynamics information represents the beat-to-beat temporal change of electrophysiological modifications in ventricular repolarization, which is shown to be sensitive to the variance during myocardial ischemia. Cardiodynamicsgram (CDG) is proposed as the three-dimensional graphic representation of cardiac dynamics information.

RESULTS

Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia.

CONCLUSION

Anormal dynamics of cardiac repolarization during ischemia can be detected using a deterministic learning-based methodology. The extracted cardiac dynamics information within routine ECG is expected to provide early detection for latent ischemia before obvious pathological changes are present in ECG.

SIGNIFICANCE

The proposed techniques can be considered as a complementary tool to the generally accepted ECG method for detection of abnormal dynamics in cardiac repolarization, which are important for identifying patients at risk of myocardial ischemia.

Design of an optimum Poincaré plane for extracting meaningful samples from EEG signals

Biosignals are considered as important sources of data for diagnosing and detecting abnormalities, and modeling dynamics in the body. These signals are usually analyzed using features taken from time and frequency domain. In theory' these dynamics can also be analyzed utilizing Poincaré plane that intersects system's trajectory. However' selecting an appropriate Poincaré plane is a crucial part of extracting best Poincaré samples. There is no unique way to choose a Poincaré plane' because it is highly dependent to the system dynamics. In this study, a new algorithm is introduced that automatically selects an optimum Poincaré plane able to transfer maximum information from EEG time series to a set of Poincaré samples. In this algorithm' EEG time series are first embedded; then a parametric Poincaré plane is designed and finally the parameters of the plane are optimized using genetic algorithm. The presented algorithm is tested on EEG signals and the optimum Poincaré plane is obtained with more than 99% data information transferred. Results are compared with some typical method of creating Poinare samples and showed that the transferred information using with this method is higher. The generated samples can be used for feature extraction and further analysis.

A multichannel decision-level fusion method for T wave alternans detection

Sudden cardiac death (SCD) is one of the most prominent causes of death among patients with cardiac diseases. Since ventricular arrhythmia is the main cause of SCD and it can be predicted by T wave alternans (TWA), the detection of TWA in the body-surface electrocardiograph (ECG) plays an important role in the prevention of SCD. But due to the multi-source nature of TWA, the nonlinear propagation through thorax, and the effects of the strong noises, the information from different channels is uncertain and competitive with each other. As a result, the single-channel decision is one-sided while the multichannel decision is difficult to reach a consensus on. In this paper, a novel multichannel decision-level fusion method based on the Dezert-Smarandache Theory is proposed to address this issue. Due to the redistribution mechanism for highly competitive information, higher detection accuracy and robustness are achieved. It also shows promise to low-cost instruments and portable applications by reducing demands for the synchronous sampling. Experiments on the real records from the Physikalisch-Technische Bundesanstalt diagnostic ECG database indicate that the performance of the proposed method improves by 12%-20% compared with the one-dimensional decision method based on the periodic component analysis.

Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indices, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indices in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indices which are tackled from the aforementioned viewpoints, namely, heart rate turbulence (HRT), heart rate variability (HRV), and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

Evaluating epicardial mapping electrogram by the method of dominant frequency and Lorenz plot

The methods of dominant frequency and Lorenz plot are used in this study to evaluate the activation rate and the activation rate variability of cardiac signals during atrial fibrillation. An epicardial mapping system was applied to acquire the atrial electrogram of mongrel dogs. The dominant frequency and Lorenz plot of each signal from various myocardial regions of the atria were analyzed. Our results show that both a frequency gradient and a variability gradient exist in the atria and the roots of pulmonary veins. The dominant frequencies of the anterior atria are higher than the posterior ones and the activation variability of both atria was higher than those of the pulmonary veins. A combination of these two methods may provide a more comprehensive understanding of the electrophysiology mechanism associated with atrial fibrillation.

Performance evaluation of diverse T-wave alternans estimators under variety of noise characterizations and alternans distributions

Prognostic significance of microvolt T-wave alternans (TWA) has been established since their inclusion among important risk stratifiers for sudden cardiac death. Signal processing schemes employed for TWA estimation have their peculiar theoretical assumptions and reported statistics. An unbiased comparison of all these techniques is still a challenge. Choosing three classical schemes, this study aims to achieve holistic performance evaluation of diverse TWA estimators from a three dimensional standpoint, i.e., estimation statistics, alternan distribution and ECG signal quality. Three performance indices called average deviation (ϑ( L )), moment of deviation (ϑ( m )) and coefficient of deviation ([Formula: see text]) are devised to quantify estimator performance and consistency. Both synthetic and real physiological noises, as well as variety of temporal distributions of alternan waveforms are simulated to evaluate estimators' responses. Results show that modification of original estimation statistics, consideration of relevant noise models and a priori knowledge of alternan distribution is necessary for an unbiased performance comparison. Spectral method proves to be the most accurate for stationary TWA, even at SNRs as low as 5 dB. Correlation method's strength lies in accurately detecting temporal origins of multiple alternan episodes within a single analysis window. Modified moving average method gives best estimation at lower noise levels (SNR >25 dB) for non-stationary TWA. Estimation of both MMAM and CM is adversely effected by even small baseline drifts due to respiration, although CM gives considerably higher deviation levels than MMAM. Performance of SM is only effected when fundamental frequency of baseline drift due to respiration falls within the estimation band around 0.5 cpb.

A class of Monte-Carlo-based statistical algorithms for efficient detection of repolarization alternans

Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices.

Computer software tool for heart rate variability (HRV), T-wave alternans (TWA) and heart rate turbulence (HRT) analysis from ECGs

Background

This paper presents a software package for quantitative evaluation of heart rate variability (HRV), heart rate turbulence (HRT), and T-wave alternans (TWA) from ECG recordings. The software has been developed for the purpose of scientific research rather than clinical diagnosis.

Material/Methods

The software is written in Matlab Mathematical Language. Procedures for evaluation of HRV, HRT and TWA were implemented. HRV analysis was carried out by applying statistical and spectral parametric and nonparametric methods. HRT parameters were derived using the Schmidt algorithm. TWA analysis was performed both in spectral and in time domain by applying Poincare mapping. A flexibility of choosing from a number of classical modelling approaches and their modifications was foreseen and implemented. The software underwent preliminary verification tests both on ECGs from the Physionet online ECG signal repository and recordings taken at the Department of Electrocardiology of the Medical University Hospital in Lodz.

Results

The result of the research is a program enabling simultaneous analysis of a number of parameters computed from ECG recordings with the use of the indicated analysis methods. The program offers options to preview the intermediate results and to alter the preprocessing steps.

Conclusions

By offering the possibility to cross-validate the results of analyses obtained by several methods and to preview the intermediate analysis steps, the program can serve as a helpful aid for clinicians in comprehensive research studies. The software tool can also be utilized in training programs for students and medical personnel.

T wave alternans evaluation using adaptive time-frequency signal analysis and non-negative matrix factorization

Each year 400,000 North Americans die from sudden cardiac death (SCD). Identifying those patients at risk of SCD remains a formidable challenge. T wave alternans (TWA) evaluation is emerging as an important tool to risk stratify patients with heart diseases. TWA is a heart rate dependent phenomenon that manifests on the surface electrocardiogram (ECG) as a change in the shape or amplitude of the T wave every second heart beat. The presence of large magnitude TWA often presages lethal ventricular arrhythmias. Because the TWA signal is typically in the microvolt range, accurate detection algorithms are required to control for confounding noise and changing physiological conditions (i.e. data nonstationarity). In this study, we address the limitations of two common TWA estimation methods, spectral method (SM) and modified moving average (MMA). To overcome their limitations, we propose a modified TWA quantification framework, called Adaptive SM, that uses non-linear time-frequency distribution (TFD). In order to increase the robustness of TWA detection in ambulatory ECGs, we also propose a new technique, called non-negative matrix factorization (NMF)-Adaptive SM. We present the analytical background of these methods, and evaluate their accuracy in detecting synthetic TWA signal in simulated and real-world ambulatory ECG recordings under conditions of noise and data non-stationarity. The results of the numerical simulations support the effectiveness of the proposed approaches for TWA analysis, which may ultimately improve SCD risk assessment.

Visualizing the electrocardiogram through orbital transform

The purpose of this work is to present a new electrocardiogram (ECG) visualization technique through a mathematical transform applied to one lead ECG signal. This transform, called orbital transform (OT) in this work, consists of a remapping of the ECG signal to a spatial curve represented by a three dimensional phase portrait. With this spatial curve it is possible to observe the evolution of changes in the QRS complex, which are not always clear to distinguish by a simple visual inspection of the temporal ECG signal. Moreover, using the projection of this curve in one of the 2D planes it is possible to easily calculate variation ranges of several ECG parameters such as maximal and minimal waves amplitudes and maximal and minimal wave slopes.

Correlation method versus enhanced modified moving average method for automatic detection of T-wave alternans

Enhanced modified moving average method (EMMAM) and correlation method (CM) for microvolt TWA identification are compared by aid of simulated ECG tracings (cases of absence of TWA and presence of stationary or time-varying TWA) and ECG recordings from healthy subjects (H-group) and patients who survived an acute myocardial infarction (AMI-group). The two competing methods were found to be equivalent when analyzing clean ECGs affected by stationary TWA. Non-stationary TWA is correctly tracked by the CM, whereas it is identified as stationary by the EMMAM. Moreover, the EMMAM suffers for its tendency to identify as TWA noise and other kinds of repolarization variability. Such limitation is most likely the cause of its false-positive TWA production. Finally, only the CM incorporates a local threshold criterion in the TWA detection algorithm which allows better discrimination between H and AMI groups, who are well known to be at increased risk to develop TWA.

Enhanced modified moving average analysis of T-wave alternans using a curve matching method: a simulation study

T-wave alternans (TWA) are beat-to-beat amplitude oscillations in the T-waves of electrocardiograms (ECGs). Numerous clinical studies have demonstrated the link between these oscillations and ventricular arrhythmias. Several methods have been developed in recent years to detect and quantify this important feature. Most methods estimate the amplitude differences between pairs of consecutive T-waves. One such method is known as modified moving average (MMA) analysis. The TWA magnitude is obtained by means of the maximum absolute difference of even and odd heartbeat series averages computed at T-waves or ST-T complexes. This method performs well for different levels of TWA, noise, and phase shifts, but it is sensitive to the alignment of the T-waves. In this paper we propose a preprocessing stage for the MMA method to ensure an optimal alignment of such averages. The alignment is performed by means of a continuous time warping technique. Our assessment study demonstrates the improved performance of the proposed algorithm.

T-wave alternans analysis improvement by means of curve alignment prior to distance calculation

Tracking of repolarization instabilities in the ECG, such as T-wave alternans (TWA), has become a popular non-invasive method to assess the vulnerability to malignant arrhythmic events. These instabilities are usually characterized by small amplitude changes and their measurement is difficult due to the presence of noise and artifacts. Several methods have been recently proposed to address this problem. Most of them are based on amplitude analysis of beat-to-beat alternation of the T wave. This paper describes a preprocessing stage intended to be used prior to amplitude analysis and aimed at improving the alignment between consecutive T waves. This increases the accuracy of the difference calculation.

Beat to beat analysis of mechanical heart valves by means of return map

Three mechanical heart valves (two bileaflet prostheses and a tilting one) were investigated in a basic hardware setup in order to evaluate with a hydrophone their opening and closing action in time and in amplitude of each beat. The recorded signal was then segmented into the series of cycles xi(t) having a temporal duration equal to the working period imposed on the valve. Two return maps were defined, in order to evaluate the degree of dispersion of the resulting scatter plot: (i) the amplitude map xi(t) versus xi+1(t); (ii) the delay map for the closure of the valve within each beat versus the successive ones. To evaluate the results obtained, two indices were proposed based on both the degree of dispersion and the deviation of the regression line of the resulting scatter plot with respect to the bisector of the map plane. The tilting disc valve showed a lower degree of dispersion, both in the amplitude signal and in the closure time delays, with respect to the other two bileaflet heart valves. The methodology proposed here could be regarded as an alternative non-invasive tool to investigate the dynamic behaviour of prosthetic heart valves, especially in the case of their suspected failure.

Methodological principles of T wave alternans analysis: a unified framework

Visible T wave alternans (TWA) in the electrocardiogram (ECG) had been regarded as an infrequent phenomenon during the first 80 years of electrocardiography. Nevertheless, computerized analysis changed this perception. In the last two decades, a variety of techniques for automatic TWA analysis have been proposed. These techniques have allowed researchers to detect nonvisible TWA in a wide variety of clinical and experimental conditions. Such studies have recently shown that TWA is related to cardiac instability and increased arrhythmogenicity. Comparison of TWA analysis methods is a difficult task due to the diversity of approaches. In this paper, we propose a unified framework which holds the existing methods. In the light of this framework, the methodological principles of the published TWA analysis schemes are compared and discussed. This framework may have an important role to develop new approaches to this problem.