4,333 research outputs found
Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals
A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Non-invasive Detection and Compression of Fetal Electrocardiogram
Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single‐channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F‐score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence
A novel semi-blind signal extraction approach incorporating parafac for the removal of eye-blink artifact from EEGs
In this paper, a novel iterative blind signal extraction (BSE) scheme for the removal of the eye-blink artifact from electroencephalogram (EEC) signals is proposed. In this method, in order to remove the artifact, the signal extraction algorithm is provided with a priori information, i.e., an estimation of the column of the mixing matrix corresponding to the eye- blink source. The a priori knowledge, namely the vector corresponding to the spatial distribution of the eye-blink factor, is identified by using the method of parallel factor analysis (PARAFAC). Hence, we call the BSE approach, semi- blind signal extraction (SBSE). The results demonstrate that the proposed algorithm effectively identifies and removes the eye-blink artifact from raw EEC measurements
A novel semiblind signal extraction approach for the removal of eye-blink artifact from EEGs
A novel blind signal extraction (BSE) scheme for the removal of eye-blink artifact from electroencephalogram (EEG) signals is proposed. In this method, in order to remove the artifact, the source extraction algorithm is provided with an estimation of the column of the mixing matrix corresponding to the point source eye-blink artifact. The eye-blink source is first extracted and then cleaned, artifact-removed EEGs are subsequently reconstructed by a deflation method. The a priori knowledge, namely, the vector, corresponding to the spatial distribution of the eye-blink factor, is identified by fitting a space-time-frequency (STF) model to the EEG measurements using the parallel factor (PARAFAC) analysis method. Hence, we call the BSE approach semiblind signal extraction (SBSE). This approach introduces the possibility of incorporating PARAFAC within the blind source extraction framework for single trial EEG processing applications and the respected formulations. Moreover, aiming at extracting the eyeblink artifact, it exploits the spatial as well as temporal prior information during the extraction procedure. Experiments on synthetic data and real EEG measurements confirm that the proposed algorithm effectively identifies and removes the eye-blink artifact from raw EEG measurements
A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
The detection of abnormal fetal heartbeats during pregnancy is important for
monitoring the health conditions of the fetus. While adult ECG has made several
advances in modern medicine, noninvasive fetal electrocardiography (FECG)
remains a great challenge. In this paper, we introduce a new method based on
affine combinations of adaptive filters to extract FECG signals. The affine
combination of multiple filters is able to precisely fit the reference signal,
and thus obtain more accurate FECGs. We proposed a method to combine the Least
Mean Square (LMS) and Recursive Least Squares (RLS) filters. Our approach found
that the Combined Recursive Least Squares (CRLS) filter achieves the best
performance among all proposed combinations. In addition, we found that CRLS is
more advantageous in extracting FECG from abdominal electrocardiograms (AECG)
with a small signal-to-noise ratio (SNR). Compared with the state-of-the-art
MSF-ANC method, CRLS shows improved performance. The sensitivity, accuracy and
F1 score are improved by 3.58%, 2.39% and 1.36%, respectively.Comment: 5 pages, 4 figures, 3 table
Single-trial event-related potential extraction through one-unit ICA-with-reference.
Objective: In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. Approach: In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Main results: Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. Significance: In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application
Investigation of GPGPU for use in processing of EEG in real-time
The purpose of this thesis was to investigate the use of General Purpose computing on Graphics Processing Units (GPGPU) to process electroencephalogram (EEG) signals in real-time. The main body of this work required the implementation of Independent Component Analysis were investigated: FastICA and JADE. Both were implemented three times: first using M-file syntax to serve as a benchmark, next, as native C code to measure performance of the algorithms when running natively on a CPU, and finally, as GPGPU code using the NVIDIA CUDA C language extension. In previous works, Independent Component Analysis represented the largest roadblock to achieving the real-time goal of processing 10 seconds of EEG within a 10 second window. It was found that both FastICA and JADE see speedups, with a maximum measured speedup of approximately 6x for FastICA, and approximately 2.5x for JADE, when operating on the largest datasets. In addition, speedups of between 1x and 2x were seen when working on datasets of the expected size provided by 10 seconds of 32-channel EEG sampled at 500 Hz. However, it was also found that GPGPU solutions are not necessary for real-time performance on a modern desktop computer as the FastICA algorithm is capable of a worst-case performance of between approximately 1 and 2 seconds depending on configuration parameters
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