Using the Wavelet Transform for T-wave alternans detection

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.

Improved modified moving average analysis of T-wave alternans using least squares-based curve fitting method

T-wave alternans (TWA) in surface electrocardiograph (ECG) signal is considered a marker of abnormal ventricular function which may be associated with ventricular tachycardia. Several methods have been developed in recent years to evaluate the important feature. One such method is known as modified moving average (MMA) analysis, which performs well for different levels of TWA, but it is sensitive to the noise in T-waves. In this paper we propose an improved MMA algorithm, which adds a stage of T-wave curve fitting for the MMA method before intermediate averaging. The curve fitting is performed by means of least square method technique. Our assessment study demonstrates the improved performance.

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.