Least Dependent Component Analysis Based on Mutual Information

We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based Least dependent Component Analysis) algorithm to a real-world dataset, the ECG of a pregnant woman. The software implementation of the MILCA algorithm is freely available at http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press

Extraction of ECGs for twin pregnancies

The wellbeing of a fetus or fetuses can be monitored by the fetal heart rate (fHR). There are several proposed methods for fHR monitoring; these include fetal phonocardiography (fPCG), fetal cardiography (fCTG) and fetal magnetocardiogram (fMCG). Although, according to the research reviewed, none of these methods are ideal for monitoring or estimating fHR. The fPCG method is highly sensitive to noise and can only be used late in the pregnancy. With fCTG, the ultrasound transducer used for measuring the fHR needs to be properly aligned, otherwise the maternal heart rate (mHR) can be recorded instead of the fHR. In addition, the ultrasound high frequency exposure is not completely proven to be safe for the fetus. fMCG can detect fHR very accurately in comparison to the other methods but the method is unwieldy and expensive; thus not widely used in a clinical environment. Therefore, there is a need for technology which would be able to provide more information about the cardiac health of a fetus, delivered in a cost-effective, streamlined manner. Based on the research reviewed and captured within this dissertation, non-invasive fetal electrocardiography (fECG) has been identified as a promising fetal cardiac monitoring method and if researched further, has the potential to become the next mainstream approach for monitoring fetal health. Within this dissertation, the fECG extraction methods have been explored and the findings captured. The research revealed that the fECG method can be used from early stages of pregnancy (20 weeks gestational age onwards). It is relatively low cost and does not necessarily require a highly skilled user. Continuous monitoring is also possible. The main challenge identified when using the non-invasive fECG extraction method is poor Signal-to-Noise Ratio (SNR) of the fECG signal on the abdominal signal which consists of fECG, maternal ECG (mECG) and noise. Eleven different fECG extraction methods were tested as part of this dissertation. The extraction methods were based on Adaptive Methods (AM), Template Subtraction (TS)or Blind Source Separation (BSS). Synthetic test signals were used for the testing the methods. The test signals included five different noise levels across seven different single pregnancy physiological cases and one twin pregnancy case. Each recording included 34 channels (32 abdominal and two maternal reference channels). For single pregnancy cases all of the extraction methods were able to extract the fECG from the test signals with varying degrees of success. Overall, the BSS-JADE method was the top performing method for single pregnancy cases getting a median F1 score of 99.85%. Furthermore, the twin pregnancy case was tested using BSS methods. The BSS FastICA algorithm using symmetric approach was the top performing method for the twin pregnancy case receiving a median F1 score of 99.93%

Robust 3-way tensor decomposition and extended state Kalman filtering to extract fetal ECG

International audienceThis paper addresses the problem of fetal electrocardiogram (ECG) extraction from multichannel recordings. The proposed two-step method, which is applicable to as few as two channels, relies on (i) a deterministic tensor decomposition approach, (ii) a Kalman filtering. Tensor decomposition criteria that are robust to outliers are proposed and used to better track weak traces of the fetal ECG. Then, the state parameters used within an extended realistic nonlinear dynamic model for extraction of N ECGs from M mixtures of several ECGs and noise are estimated from the loading matrices provided by the first step. Application of the proposed method on actual data shows its significantly superior performance in comparison to the classic methods

Robust 3-way tensor decomposition and extended state Kalman filtering to extract fetal ECG

International audienceThis paper addresses the problem of fetal electrocardiogram (ECG) extraction from multichannel recordings. The proposed two-step method, which is applicable to as few as two channels, relies on (i) a deterministic tensor decomposition approach, (ii) a Kalman filtering. Tensor decomposition criteria that are robust to outliers are proposed and used to better track weak traces of the fetal ECG. Then, the state parameters used within an extended realistic nonlinear dynamic model for extraction of N ECGs from M mixtures of several ECGs and noise are estimated from the loading matrices provided by the first step. Application of the proposed method on actual data shows its significantly superior performance in comparison to the classic methods

Efficient Blind Source Separation Algorithms with Applications in Speech and Biomedical Signal Processing

Blind source separation/extraction (BSS/BSE) is a powerful signal processing method and has been applied extensively in many fields such as biomedical sciences and speech signal processing, to extract a set of unknown input sources from a set of observations. Different algorithms of BSS were proposed in the literature, that need more investigations, related to the extraction approach, computational complexity, convergence speed, type of domain (time or frequency), mixture properties, and extraction performances. This work presents a three new BSS/BSE algorithms based on computing new transformation matrices used to extract the unknown signals. Type of signals considered in this dissertation are speech, Gaussian, and ECG signals. The first algorithm, named as the BSE-parallel linear predictor filter (BSE-PLP), computes a transformation matrix from the the covariance matrix of the whitened data. Then, use the matrix as an input to linear predictor filters whose coefficients being the unknown sources. The algorithm has very fast convergence in two iterations. Simulation results, using speech, Gaussian, and ECG signals, show that the model is capable of extracting the unknown source signals and removing noise when the input signal to noise ratio is varied from -20 dB to 80 dB. The second algorithm, named as the BSE-idempotent transformation matrix (BSE-ITM), computes its transformation matrix in iterative form, with less computational complexity. The proposed method is tested using speech, Gaussian, and ECG signals. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with other approaches used in the dissertation. The third algorithm, named null space idempotent transformation matrix (NSITM) has been designed using the principle of null space of the ITM, to separate the unknown sources. Simulation results show that the method is successfully separating speech, Gaussian, and ECG signals from their mixture. The algorithm has been used also to estimate average FECG heart rate. Results indicated considerable improvement in estimating the peaks over other algorithms used in this work

Hybrid methods based on empirical mode decomposition for non-invasive fetal heart rate monitoring

This study focuses on fetal electrocardiogram (fECG) processing using hybrid methods that combine two or more individual methods. Combinations of independent component analysis (ICA), wavelet transform (WT), recursive least squares (RLS), and empirical mode decomposition (EMD) were used to create the individual hybrid methods. Following four hybrid methods were compared and evaluated in this study: ICA-EMD, ICA-EMD-WT, EMD-WT, and ICA-RLS-EMD. The methods were tested on two databases, the ADFECGDB database and the PhysioNet Challenge 2013 database. Extraction evaluation is based on fetal heart rate (fHR) determination. Statistical evaluation is based on determination of correct detection (ACC), sensitivity (Se), positive predictive value (PPV), and harmonic mean between Se and PPV (F1). In this study, the best results were achieved by means of the ICA-RLS-EMD hybrid method, which achieved accuracy(ACC) > 80% at 9 out of 12 recordings when tested on the ADFECGDB database, reaching an average value of ACC > 84%, Se > 87%, PPV > 92%, and F1 > 90%. When tested on the Physionet Challenge 2013 database, ACC > 80% was achieved at 12 out of 25 recordings with an average value of ACC > 64%, Se > 69%, PPV > 79%, and F1 > 72%.Web of Science8512185120

Blind source separation of underdetermined mixtures of event-related sources

International audienceThis paper addresses the problem of blind source separation for underdetermined mixtures (i.e., more sources than sensors) of event-related sources that include quasi-periodic sources (e.g., electrocardiogram (ECG)), sources with synchronized trials (e.g., event-related potentials (ERP)), and amplitude-variant sources. The proposed method is based on two steps: (i) tensor decomposition for underdetermined source separation and (ii) signal extraction by Kalman filtering to recover the source dynamics. A tensor is constructed for each source by synchronizing on the ''event'' period of the corresponding signal and stacking different periods along the second dimension of the tensor. To cope with the interference from other sources that impede on the extraction of weak signals, two robust tensor decomposition methods are proposed and compared. Then, the state parameters used within a nonlinear dynamic model for the extraction of event-related sources from noisy mixtures are estimated from the loading matrices provided by the first step. The influence of different parameters on the robustness to outliers of the proposed method is examined by numerical simulations. Applied to clinical electroencephalogram (EEG), ECG and magnetocardiogram (MCG), the proposed method exhibits a significantly higher performance in terms of expected signal shape than classical source separation methods such as piCA and FastICA

Non Invasive Foetal Monitoring with a Combined ECG - PCG System

Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative