Electrocardiogram compression using the nonlinear iterative partial least squares algorithm: a comparison between adaptive and non-adaptive approach

Abstract

Data compression is applicable in reducing amount of data to be stored and it can be applied in several data collecting processes, being generated by lossy or lossless compression algorithms. Due to its large amount of data, the use of compression is desirable in ECG signals. In this work, we present the accepted nonlinear iterative partial least squares (NIPALS) method as an option to ECG compression method, as recommended by Nicolosi (1999). In addition, we compare the results based in an adaptive and non-adaptive version of this method, by using the MIT arrhythmia database. As a help to obtain a better comparison, we have developed an abnormality indicator related to possible abnormalities in the waveform and a decision method that helps to choose between adaptive or non-adaptive approach. Results showed that the adaptive approach is better than the non-adaptive approach, for the NIPALS compression algorithm.