Adaptive estimation of Karhunen-Loeve series applied to the study of ischemic ECG records

The Karhunen-Loeve transform (KLT) has been used as a tool to analyze the repolarization period in the study of ischemic episodes. The dynamic variations in the ST-T complex shape are shown as variations of the kl series associated with the beat series. Here, the authors propose an adaptive system to estimate the kl coefficients of the ST-T complex in order to improve the signal-to-noise ratio (SNR) of the estimation (it is obtained around 10 dB of improvement). A transversal adaptive linear combiner filter using as reference inputs the KL basis functions and as primary input the concatenation of noisy ST-T completes from consecutive beats is used. It is shown how the weights of the filter become, after convergence, estimates of the kl series. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are studied and compared in the kl time series estimation. It is presented a specific initialization for the LMS which leads to the same performance than the RLS.