Simultaneous localization and separation of biomedical signals by tensor factorization

Abstract

In this paper, we introduce mathematical models based on multi-way data construction and analysis with a goal of simultaneously separating and localizing the sources in the brain by analysis of scalp electroencephalogram (EEG) data. we address the problem of EEG source separation and localization through a 3-way tensor analysis. We represent multi-channel EEG data using a third-order tensor with modes: space (channels), time samples and number of segments. Then we demonstrate that multi-way analysis techniques, in particular PARAFAC2, can successfully separate and localize disjoint sources within the brain. Also we used this method for separation of maternal and fetal ECG signals.

Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm

“…Figure 9 shows the simulated original artificial signals ( ) for EEG and different types of artifacts EOG, ECG, and LN. Eye blink artifact is simulated using Sinc function [45,47], ECG artifact is simulated using ecg function in Matlab, and the power line noise interference is simulated based on sinusoidal function (50 Hz). Figure 10 shows the signals with zero mean and unit variance.…”

Method of fetal electrocardiogram extraction based on ν ‐support vector regression

“…Some signal processing methods have been used in FECG extraction, such as the adaptive filter [2,[7][8][9], event synchronous canceller (ESC) [10][11][12], singular value decomposition (SVD) [13,14], blind source separation (BSS) [15][16][17] and artificial neural network (ANN) [3,4,18]. Whereas BSS requires multiple leads to collect several electrocardiogram (ECG) signals [19].…”