Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings

2016 24th European Signal Processing Conference (EUSIPCO)

Nandi

2016

In this paper, we propose a strategy to estimate a Fetal Electrocardiogram (FECG) subspace from a set of mixed ECG recordings from the thoracic and abdominal electrodes attached on a pregnant woman. The ECGs from an expectant woman contain FECG that can provide valuable information for fetal health monitoring, such as the fetal heart rate (FHR). After applying blind source separation (BSS) methods to mixed ECG, a number of independent components are obtained. The main purpose of this paper is to classify an FECG group from all of these components which can be classified as FECG, MECG and noise according to the features of signals. This work is inspired by the concept of multidimensional independent component analysis (MICA). In order to automate the classification task, we propose a procedure based on cyclostationarity of FECGs; in particular, we propose an integrated Cyclic Coherence as a criterion to classify FECG subspace automatically. The method is validated on real world DaISy dataset and the results are promising.

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fetal ECG subspace estimation based on cyclostationarity

Zhang

2016 24th European Signal Processing Conference (EUSIPCO)

Nandi

2016

“…The cyclostationarity property of the FECGs have been successfully used in previous work [14]. In order to perform MICA decomposition, the authors also proposed a method based on the Cyclic Coherence (CC) [15] which is a normalised cyclostationarity measure based on the correlation degree between two signals at each frequency value; this same measure is used in this work in order to decide whether a JADE-estimated IC belongs to the FECG rather than to the MECG subspace.…”

Section: B the Cyclostationarity Property And Measurementioning

confidence: 99%

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

Roussel

2015

IFMBE Proceedings

Abstract-In this paper, the authors propose to estimate the contribution of the electrical activity of the foetal heart to each component of cutaneous electrocardiogram (ECG) recordings using an oblique projection technique. To date, related research work reported in the literature using Blind Source Separation (BSS) or Independent Component Analysis (ICA) methods rely on a widely used projection techniques family, which is the orthogonal projection. More recently, reported work in other areas of biomedical research is based on the use of oblique projection techniques in order to remove the artifacts from the available recordings and to estimate the contribution of the Signal of Interest (SoI) to the data set. The novel approach presented in this paper is tailored to the problem of foetal ECG (FECG) contribution estimation. The optimal steering vector's computation procedure for the oblique projection is based on the use of the cyclostationary properties of the SoI and on the concept of Multidimensional Independent Component Analysis (MICA). The latter is used to gather into independent subspaces the components estimated using a BSS/ICA method and corresponding to the foetus' heartbeats, the mother's heartbeats and various artifacts and noise sources. Early comparative results with the orthogonal approach obtained after application of both methods to synthetic non-invasive FECG recordings illustrate the reliability as well as the effectiveness of the proposed method. A discussion and perspectives for future research conclude this work.

“…In particular, Blind Source Separation (BSS) [1] has been applied, e.g. for single muscle activity detection of surface electromyographic signals [2], but also, for the estimation of the foetus' heartbeat rate and subspace dimension [3]. One of the most challenging applications of this technique concerns the exploration of muscle's electrical activity.…”

Section: Introductionmentioning

confidence: 99%

A new cyclostationarity-based blind approach for motor unit's firing rate automated detection in electromyographic signals

Roussel

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

Ravier

et al. 2014

This work focuses on electromyographic (EMG) signal processing. We propose a new blind approach that aims at detecting the firing rates of the activated motor units. The proposed method is based on the fact that, EMGs can be modelled as second-order cyclostationary signals. After application of a Blind Source Separation (BSS) algorithm, we compute a cyclostationarity measure which is the Cyclic Spectral Density (CSD), and we show how one can use it to group the estimated components into independent subspaces and in an automated manner. The proposed classification procedure is based on the concept of subspace BSS techniques, like the Multidimensional Independent Component Analysis (MICA), the difference being that our method allows automatic classification of the estimated source signals. After discarding the subspace corresponding to the noise and computation of a modified CSD measure, the proposed procedure yields to the detection of specific cyclic frequencies corresponding to the discharge frequencies of the Motor Units Action Potential Trains (MUAPTs). Early results obtained from experiments on synthetic EMGs are presented in the paper and research perspectives conclude this work.