Extraction of Desired Signal Based on AR Model with Its Application to Atrial Activity Estimation in Atrial Fibrillation

Abstract

The use of electrocardiograms (ECGs) to diagnose and analyse atrial fibrillation (AF) has received much attention recently. When studying AF, it is important to isolate the atrial activity (AA) component of the ECG plot. We present a new autoregressive (AR) model for semiblind source extraction of the AA signal. Previous researchers showed that one could extract a signal with the smallest normalized mean square prediction error (MSPE) as the first output from linear mixtures by minimizing the MSPE. However the extracted signal will be not always the desired one even if the AR model parameters of one source signal are known. We introduce a new cost function, which caters for the specific AR model parameters, to extract the desired source. Through theoretical analysis and simulation we demonstrate that this algorithm can extract any desired signal from mixtures provided that its AR parameters are first obtained. We use this approach to extract the AA signal from 12-lead surface ECG signals for hearts undergoing AF. In our methodology we roughly estimated the AR parameters from the fibrillatory wave segment in the V1 lead, and then used this algorithm to extract the AA signal. We validate our approach using real-world ECG data.

The Semi-blind Source Extraction Algorithm for Array Radar Signal

“…In the noise-free case [14], we supposed that an observed stochastic signal vector x with m-dimension is the linear mixture of an l-dimensional zero-mean and unit-variance vector s, that is,…”

“…The algorithms of Liu et al [10,13] are classified as the mean cross prediction error (MCPE) algorithms that can extract the signal with minimal MSPE. In our previous work [14], we proposed a BSE algorithm whose cost function can cater for the specific AR model parameters of desired signal. Our algorithm [14] is classified as the mean square cross prediction error (MSCPE) algorithm that can extract the signal with minimal MSCPE in the noise-free case.…”

“…In our previous work [14], we proposed a BSE algorithm whose cost function can cater for the specific AR model parameters of desired signal. Our algorithm [14] is classified as the mean square cross prediction error (MSCPE) algorithm that can extract the signal with minimal MSCPE in the noise-free case. Our previous research results showed that the desired signal has always minimal MSCPE, but has not minimal MSPE.…”

“…Following Liu's research ideas [10,13], we extend the algorithm in noise -free case in [14] to a novel BSE algorithm in the noisy case and apply the novel algorithm to extract array radar signal in this paper.…”

The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease

“…The 21-electrode EEG data were first decomposed using an independent component analysis (ICA) method [31][32][33], and then the data were sorted by their kurtosis value in descending order. Finally, the subspace of EEG signal was reconstructed using back projection of only the first several components with far-Gaussian property.…”