Codificação Eficiente de Sinais de Eletrocardiograma

Abstract

Typically, in the digital processing of electrocardiography signal, linear transformations are used to turn the signals more tractable in accordance to some application. For applications such as classification or data compression, it usually aimed to reduce the redundancy present in the signals, increasing the potential of the applications. There are various methods usually used for the task, the Fourier transform, the wavelet transform and principal component analysis. All those methods have any sort of limitation, being the use of a predefined space, orthogonal spaces or the limitations to second order statistics. In this work we propose the use of the independent component analysis method for the encoding of the ECG signals, using as theoretical basis the neuroscience concept of efficient coding. Two important results were found, the basis functions space generated by the proposed method is different from the spaces seen on the usual methods, and, on average, the method can reduce the redundancy of the signal. We concluded that the traditional methods might not exploit the coding potential of ECG signals due to their limitations, and also that ICA might be a reliable method for improving the performance comparing to the current systems.