An improved statistical representation for ECG electrode movement and muscular activity noises in the context of T-wave alternan estimation

Microvolt T-wave alternans (TWA) are recognized as markers for malignant ventricular arrhythmias, leading to sudden cardiac death. Its extraordinary pathological significance and life-critical application demand elaborate modeling approaches and efficient analysis schemes. Accurate statistical model encompassing the dynamics of physiological noises and other outliers is highly significant to detection and estimation of the microvolt signal. The anomalies in parametric values characterizing the distributions of the above random phenomena are apt to incur modeling errors. Recent TWA detection theoretic approaches assume Laplacian noise due to leptokurtic distribution of electrode movement (em) and muscular activity (ma) recordings. The presented statistical analysis shows that the practiced model compromises the asymmetric nature of the probability distributions for em and ma. An analytical model called Biexponential distribution is suggested to realize the leptokurtic as well as the asymmetric nature of the noise characteristics. Comparative analysis is presented using visual inspection method, χ2 goodness-of-fit and Monte Carlo simulations. The proposed model achieves a best match of 99.14% and 98.13% for em and ma as compared to a Laplacian fit of 95.20% and 93.84%, respectively. Conversely, the worst fit values for em and ma are found to be 96.32% and 92.45% for Biexponential and 60.47% and 15.18% for Laplacian models, respectively. The augmented degree of freedom is likely to increase the complexity of the already challenging TWA detection problem; however, the proposed model achieves a more realistic representation of the real noise data by closely matching the statistical parameters.