An Open-Source Standard T-Wave Alternans Detector for Benchmarking

Short-term effects of transcutaneous auricular vagus nerve stimulation on T-wave alternans in people with focal epilepsy - An exploratory pilot study

High levels of T-wave alternans (TWA) are linked to an increased risk of sudden cardiac death. People with epilepsy display elevated TWA levels that are decreased by chronic vagus nerve stimulation via implanted devices after 2-4 weeks or later. Our objective was to explore short-term effects of transcutaneous auricular vagus nerve stimulation (tVNS) on TWA. Five patients (3 female) with focal epilepsy undergoing video-EEG monitoring were included. TWA levels were determined using a one-channel modified lead I ECG via an open-source TWA-algorithm on two consecutive days, 1 h before, during and after tVNS via the left auricle. Data are given as mean ± SE. Mean TWA at baseline was 3.8 ± 0.4 µV and 3.0 ± 0.6 µV during stimulation on day 2. Stimulations on the second day were associated with TWA reductions by 22 ± 13 % that exceeded stimulation effects on the first day relative to baseline (p < 0.05). Linear mixed-models revealed effects of both stimulation (p < 0.05) and stimulation number (p < 0.005). Normalized TWA showed reproducible peak reductions at both days within 35 min after the initiation of tVNS (p < 0.05). Our observations suggest that tVNS has short-term effects on TWA, supporting the notion that vagus nerve stimulation has a beneficial impact on electrical cardiac properties.

An Excel-based implementation of the spectral method of action potential alternans analysis

Action potential (AP) alternans has been well established as a mechanism of arrhythmogenesis and sudden cardiac death. Proper interpretation of AP alternans requires a robust method of alternans quantification. Traditional methods of alternans analysis neglect higher order periodicities that may have greater pro-arrhythmic potential than classical 2:1 alternans. The spectral method of alternans analysis, already widely used in the related study of microvolt T-wave alternans, has also been used to study AP alternans. Software to meet the specific needs of AP alternans analysis is not currently available in the public domain. An AP analysis tool is implemented here, written in Visual Basic for Applications and using Microsoft Excel as a shell. This performs a sophisticated analysis of alternans behavior allowing reliable distinction of alternans from random fluctuations, quantification of alternans magnitude, and identification of which phases of the AP are most affected. In addition, the spectral method has been adapted to allow detection and quantification of higher order regular oscillations. Analysis of action potential morphology is also performed. A simple user interface enables easy import, analysis, and export of collated results.

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.

A nonparametric surrogate-based test of significance for T-wave alternans detection

We present a nonparametric adaptive surrogate test that allows for the differentiation of statistically significant T-wave alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise-induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data, no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data nonstationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise-rejection methods used with the spectral method (SM) and the modified moving average (MMA) techniques. Using a previously described realistic multilead model of TWA and real physiological noise, we demonstrate the proposed approach that reduces false TWA detections while maintaining a lower missed TWA detection, compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases: the Normal Sinus Rhythm Database, the Chronic Heart Failure Database, and the Sudden Cardiac Death Database. Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of HRs. The most-marked difference was generally found at higher HRs, and the new technique resulted in a larger margin of separability between patient populations than when the SM or MMA were applied to the same data.

T-wave alternans patterns during sleep in healthy, cardiac disease, and sleep apnea patients

BACKGROUND

T-wave alternans (TWA) activity is known to be a function of heart rate and condition, as well as perhaps physiological state. A recently published nonparametric nonstationary TWA analysis method has been shown to reject nonstationary noise accurately using phase-randomized surrogates and has been shown to estimate TWA accurately. This new method was evaluated on multiple databases over a range of heart rates and in healthy subjects, cardiac disease patients, and obstructive sleep apnea (OSA) patients. We hypothesized that TWA would be lower than previously reported when measured with our new technique and that higher levels of TWA would be observed in OSA patients when compared with healthy subjects.

METHODS

Five databases were analyzed, as follows: (1) healthy subjects from PhysioNet's Normal Sinus Rhythm Database, (2) arrhythmia patients from PhysioNet's Chronic Heart Failure Database and (3) PhysioNet's Sudden Cardiac Death Database, (4) OSA patients from PhysioNet's MIT-BIH Polysomnographic Database, and (5) 85 subjects from a private Sleep Apnea Database. T-wave alternans magnitudes were calculated for 7 heart rate decades (intervals of 10 beats/min (bpm) between 40 and 110 bpm) for each database. The Mann-Whitney U test and the 2-sample Kolmogorov-Smirnov test were applied to test for significant differences between data from each database in each heart rate decade interval.

RESULTS

In the healthy population, TWA activity level tended to increase with heart rate. Moreover, there appeared to be an unexpected nadir in TWA activity around 60 to 70 bpm and a small but significant rise in TWA above and below these heart rates. The rise in TWA at lower heart rates has not been previously reported, to our knowledge. We also observed that TWA is unexpectedly lower in OSA patients and did not increase with heart rate.

CONCLUSION

Although the physiological mechanisms underlying our observations are unclear, there may be clinical implications for TWA testing, particularly at low heart rates, a previously overlooked aspect of TWA.

An artificial vector model for generating abnormal electrocardiographic rhythms

We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods.

An Artificial Multi-Channel Model for Generating Abnormal Electrocardiographic Rhythms

We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat are incorporated as before from varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time-and frequency-domain heart rate (HR) and heart rate variability (HRV) characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by coupling the RR interval to the angular frequency of the dipole. We demonstrate an example of the use of this model by simulating T-Wave Alternans (TWA). The magnitude of the TWA effect is modeled as a disturbance on the T-loop of the dipole with a magnitude that differs in each of the three VCG planes. The effect is then turned on or off using a HMM. The values of the transition matrix are determined by the local heart rate, such that when the HR ramps up towards 100 BPM, the probability of observing a TWA effect rapidly but smoothly increases. In this way, no 'sudden' switching from non-TWA to TWA is observed, and the natural tendency for TWA to be associated with a critical HR-related activation level is simulated. Finally, to generate multi-lead signals, the VCG is mapped to any set of clinical leads using a Dower-like transform derived from a least-squares optimization between known VCGs and known lead morphologies. ECGs with calibrated amounts of TWA were generated by this model and included in the PhysioNet/CinC Challenge 2008 data set.