Blind Source Separation Techniques Research Articles

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals.

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus's condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy.

On the Initial Phase of the Ongoing Unrest at Campi Flegrei and Its Relation with Subsidence at Vesuvio (Italy)

The densely inhabited area of Naples (Italy), between the Campi Flegrei and Vesuvio volcanoes, is one of the most hazardous regions in the world. After two decades of sustained subsidence, Campi Flegrei has been experiencing an accelerating uplift since 2005. The uplift is currently associated with unusual seismicity and increased degassing. To try to identify the cause of the shift from subsidence to uplift and explore any connection between Campi Flegrei and Vesuvio, we analysed the ground displacement time series of the two volcanoes from 1993 to 2010, obtained from ERS/ENVISAT Synthetic Aperture Radar imagery. To distinguish between the various sources of deformation, we used simple scatter plots and a blind source separation technique called variational Bayesian independent component analysis (vbICA). We obtained consistent results using both approaches. Specifically, with vbICA, we identified two significant independent components (ICs). IC1 describes the subsidence that occurred at Campi Flegrei prior to 2000, including the mini-uplifts of 2000 and 2005, and part of the post-2005 uplift. The expansion and contraction of two volumes beneath Campi Flegrei satisfy IC1: a sill-shaped volume at a depth of approximately 3 km and a small volume at a depth of 1–2 km, respectively. The two sources of deformation reproduce the large-scale deformation in the Campi Flegrei area and the local deformation in the Solfatara area, respectively. In the Campi Flegrei area, IC2 exhibits primarily uplift, which is concentrated in the eastern part of the caldera. The deformation pattern is complex and difficult to interpret. If we model it using simple spheroidal deformation sources, the pattern suggests that two volumes at depths of approximately 9 and 8 km are experiencing opposite activity, namely contraction (beneath the southwestern part of the caldera) and expansion (beneath the central part of the caldera). In the Vesuvio area, IC2 is consistent with the deformation induced by the contraction of a volume at a depth of around 9 km. The contraction beneath Vesuvio is smaller in magnitude than the expansion/contraction beneath Campi Flegrei. The correlation observed after 2002 between uplift at Campi Flegrei and subsidence at Vesuvio suggests the transfer of magma and/or magmatic fluids between the two plumbing systems at 8–9 km depth. This implies that part of the ongoing unrest at Campi Flegrei may have been promoted by mass transfer from below Vesuvio.

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

Purification of Brain Signals Using Various Blind Source Separation Techniques

There arelot of challenges when analyzing the brain signals that did not yet have a basic solution, as there are electrical activities between neurons in the brain,related to all activities in the body. This activity can be seen using a non-surgical technique that is called EEG (i.e. Electroencephalography)such as the appearance of artifactsthrough the registration process, which increases the difficulty of analyzing the signals of the brain, so the technique of blind source separation(BSS) has been used to overcome the problem of artifacts and to separate the main sources (Mixed) without making noise aroundthe original sources.. Therefore, the system for rejecting allartifacts based on the algorithms for separating the blind source has been proposed, bymaking a comparison between four separation algorithms and choosing the best ones according to criteria. After passing a datasetsimulated through thosecriteria, the proposed system can remove the artifacts including Electrocardiogram (ECG), Electrooculogram (EOG)as well as apower line noise interference (LN)) and other EEG mixtures. The proposed method's influenceis checked by two performance indexes Interference to signal ratio (ISR) and (SNR) signal to noise ratio.The results indicated that the BEFICA algorithm is the best and most efficient, as it achieved the highest ratio of VSNR among four separation algorithms due to its developmental advantages.

Enhancement of Brain Computer Interface System Based on Artificial Intelligent Technique

With the rapid development of information technology, the EEG has become an increasingly popular method for analyzing brain signals. EEG signals greatly contribute to(BCI) brain-computer communication . BCI outputs are used to restore many functions in relation to individuals with a motor impairment, and these signals are complex random signals produced from hundreds of millions of neurons in EEG mixtures in the brain that contain many data about brain activity. its include artefacts which have a great influence on the diagnosis; Hence, these unwanted signals become the most important problem in EEG signal analysis. Therefore, used four blind source separation techniques (BSS), STONE, FICA, BEFICA and EFICA. The proposed system is using one of the new Antlion (ALO) optimization algorithms to improve the performance of the previous algorithms and find out which ones are the most responsive, by comparing them and choosing the best ones according to the (PSD) standard. With the use of real data, noticed through the results that the EFICA algorithm is the best response to the improvement and the most efficient, as the ratio of (PSD) was the least possible, as ALO worked to reduce thevariance in the distribution of the frequency spectrum because it relied on solving constrained problems using various searches.

Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation

This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e., M=2, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct N>2 sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array.

A novel hybrid method for calculating the fetal heart rate from the non-invasive abdominal Electrocardiogram signal

Continuous monitoring of fetal health requires the accurate calculation of the Fetal Heart Rate (FHR) by efficiently isolating fetal Electrocardiogram (fECG) from non-invasive abdominal Electrocardiogram (aECG). There are numerous solutions proposed for this problem, having its own restrictions. This paper introduces a novel three-stage hybrid adaptive approach for fECG extraction and FHR determination from aECG. The proposed framework is an amalgamation of independent component analysis (ICA), adaptive exponential functional link network (AEFLN), and novel wavelet thresholding (NWT). The proposed work aims to determine the best quality fetal R-peaks for calculating fetal heart rate (FHR). ICA is a blind source separation technique that uses all the information from the multichannel inputs to determine the maternal Electrocardiogram (mECG) and enhanced aECG. In the next stage, a non-linear adaptive filter, AEFLN, is used for the extraction of fECG. This approach requires no additional algorithm for polarity reversal and time alignment before AEFLN. At last, NWT determines the fetal R-peak envelopes from the residual fECG. The R-peak envelopes are used to analyze FHR, and its accuracy was accessed using the FHR trace. So far, FastICA-AEFLN-NWT has not been used as a hybrid method for fECG extraction and fetal R-peak detection. The algorithm’s effectiveness was tested on both real databases from Daisy and Physionet and synthetic databases from Physionet. The proposed hybrid method attains 95.29 % of sensitivity (SE), 96.68 % of Positive Predictive Value (PPV), 95.92 % of F1 score, and FHR measurement (FHRmeas) is 29.72 bpm2. The proposed methodology outperforms the state-of-art methods in fECG extraction.

Modal identification of non‐classically damped structures using generalized sparse component analysis

SummaryModal identification method based on blind source separation (BSS) technique has gained extensive attentions for civil structures. Developing the complex modes estimation method is important in practical applications because the assumption of proportional damping is not always satisfied. Sparse component analysis (SCA) performs well in underdetermined BSS problems. However, SCA is confined to the situation of proportional damping. In this study, a generalized SCA method is proposed to extend the original SCA method to both real and complex modes identification. First, the general formulation of complex modes is extended by the analytic form to eliminate the complex conjugate part in the BSS model. A new single‐source‐point detection method that is available to handle real and complex modes is proposed. Local outlier factor method is adopted to remove the outliers in single source points. Subsequently, complex‐valued modal matrix is calculated by the clustering technique. Then, modal responses are recovered using the complex version of smoothed zero norm method, where modal frequencies and damping ratios can be extracted. Finally, the effectiveness of the proposed method is demonstrated for identification of real and complex modes, close modes, and underdetermined problem. The application to a benchmark structure demonstrates the effectiveness for practical applications.

A novel semi-blind source separation framework towards maximum signal-to-interference ratio

For the independence-based blind source separation (BSS) methods, effective modeling of the source signals is important. When applying the auxiliary function techniques, different source models normally result in different forms of the weighted covariance matrices, which in turn are used to compute the demixing filters. This paper proposes a new algorithmic framework termed minimum variance independent component analysis (MVICA), which is rigorously derived from the conventional BSS methods by determining the weighted covariance matrices with the maximum signal-to-interference ratio (SIR) criterion.The maximum-SIR weighted covariance matrix is approximately proved to be the interference covariance matrix. A deep neural network-supported implementation of the proposed MVICA algorithm is subsequently presented. Furthermore, we give a revealing insight into the relationships between MVICA and the existing speech separation approaches including conventional BSS methods and several mainstream beamforming techniques. Experimental results show the superiority of MVICA over the state-of-the-art BSS techniques and beamformers under various conditions, in terms of not only SIR but also signal-to-distortion ratio, speech intelligibility and perceptual quality and automatic speech recognition accuracy.

A survey of artificial intelligence approaches in blind source separation

In various signal processing applications, such as audio signal recovery, the extraction of desired signals from a mixture of other signals is a crucial task. To achieve superior performance and efficiency in separator systems, extensive research has been conducted. Blind source separation emerges as a relevant technique to address the challenge of separating and reconstructing unknown signals when only observations of their mixtures are available to end-users. Blind source separation involves retrieving a set of independent source signals mixed by an unknown and potentially destructive combining system. Notably, the separation process in blind source separation frameworks solely relies on observing the mixed sources without prior knowledge of the mixing algorithm or the source signal characteristics. The significance of blind source separation has garnered substantial attention, and its numerous applications have been demonstrated, which serves as the primary motivation for conducting this comprehensive study. This paper presents a systematic literature survey of blind source separation, encompassing existing methods, approaches, and applications, with a particular focus on artificial intelligence-based frameworks. Through a thorough review and examination, this work sheds light on the diverse techniques utilized in blind source separation and their performance in real-world scenarios. The study identifies research gaps in the current literature, highlighting areas that warrant further investigation and improvement. Moreover, potential avenues for future research are outlined to contribute to the ongoing development of blind source separation techniques.

Development of a speech separation system using frequency domain blind source separation technique

Development of a speech separation system using frequency domain blind source separation technique

Bridge Damage Detection Using Complexity Pursuit and Extreme Value Theory

Bridge structures are susceptible to environmental and operational variations (EOVs). Improperly handling these influences may result in incorrect assessments of the bridge’s health condition. Blind source separation (BSS) techniques show promising potential in suppressing the effects of EOVs. However, major challenges such as high data variability, difficulty in parameter selection, lack of reliable decision thresholds, and practical engineering validation have seriously hindered the application of such techniques in bridge health monitoring. Consequently, this paper proposes a new method for bridge damage detection that combines complexity pursuit (CP) and extreme value theory (EVT). This method first uses the exponentially weighted moving average (EWMA) technique to preprocess the measured modal frequencies. The CP algorithm and information entropy are then used to extract structural damage sources from the preprocessed data automatically. Based on the extracted structural damage sources, the damage index (DI) is defined using k-means clustering and Euclidean distance. Following that, the generalized extreme value (GEV) distribution is used to fit the DI data under the normal condition of the bridge, and the damage detection threshold is given according to the fitted distribution. Benchmark data of the KW51 railway bridge are considered to verify the effectiveness of the proposed method along with several comparative studies. The results show that even under strong EOV influences, the proposed method still maintains good damage detection accuracy and robustness, and its effectiveness is superior to some well-known damage detection methods.

Dynamic parameters identification of 3D sandwich wall panels from phase-based video measurement via smartphones camera

This study, unlike prior studies, utilizes a smartphone camera as a high-speed camera for output-only operational modal analysis by using video measurement and advanced computer vision algorithms. Besides, the accelerometer sensors of the three smartphones collect the acceleration data. In contrast to recent research, this study has been conducted outdoors without any additional light setup and solid color background to contrast the motions. First, multi-scale image processing and blind source separation techniques are applied to each video frame to obtain the modal coordinates. The logarithm decrement technique and Fourier transform extract the damping ratios and frequencies of dominant modes of structures. To validate the smartphone camera measurements, two 3D sandwich walls were used and their results compare with accelerometer data. After that, the modal properties of a cantilever steel beam were identified by only smartphone camera measurements. The obtained modal parameters from smartphones camera and accelerometers are in excellent agreement, results indicated that. This study showed that smartphones camera has great potential to use as a non-contact modal analysis facility.

Investigation of Modal Identification of Frame Structures Using Blind Source Separation Technique Based on Vibration Data

This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation.

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation.

Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configurat- ion is recognized in 92.2% of the subjects. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

Isolation of multiple electrocardiogram artifacts using independent vector analysis.

Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

Blind separation of noisy piecewise-stationary mixtures via probability measure transform

In this paper, we consider the problem of blind source separation (BSS) under non-Gaussian impulsive noise. We consider the case of overdetermined instantaneous-linear-mixtures of piecewise-stationary signals. These are corrupted by additive stationary noise. Under this framework, we propose a two-stage separation method, called measure-transformed BSS (MT-BSS), that applies a transform to the probability distribution associated with each data segment. The generating function of the transform at hand is a non-negative function, called MT-function, that weights the data points. We show that proper choice of the involved MT-functions can lead to enhanced separation performance. The performance advantage of MT-BSS over alternative BSS techniques is illustrated in simulation examples. In these studies, we consider synthetic data and real audio signals.

EEG Artifact Removal Using Canonical Correlation Analysis and EMD-DFA based Hybrid Denoising Approach

EEG signal recordings are often contaminated by Ocular and Muscular artifacts. Such artifacts obscure the signal's important information and complicate the diagnosis. Thus, before utilising the recorded EEG signal for any kind of diagnosis, the signal must be pre-processed to eliminate the artifacts that have contaminated the signal. In this paper a hybrid approach for denoising the EEG signal is proposed. The approach is based on Canonical Correlation Analysis (CCA) as a Blind Source Separation (BSS) technique for elimination of muscular artifacts. In the next step, DFA thresholded EMD process is utilized for the rejection of ocular artifacts. The performance of the proposed hybrid approach is compared with existing techniques on the basis of different performance metrics.

Post-acquisition water-signal removal in 3D water-unsuppressed 1 H-MR spectroscopic imaging of the prostate.

To develop a robust processing procedure of raw signals from water-unsuppressed MRSI of the prostate for the mapping of absolute tissue concentrations of metabolites. Water-unsuppressed 3D MRSI data were acquired from a phantom, from healthy volunteers, and a patient with prostate cancer. Signal processing included sequential computation of the modulus of the FID to remove water sidebands, a Hilbert transformation, and k-space Hamming filtering. For the removal of the water signal, we compared Löwner tensor-based blind source separation (BSS) and Hankel Lanczos singular value decomposition techniques. Absolute metabolite levels were quantified with LCModel and the results were statistically analyzed to compare the water removal methods and conventional water-suppressed MRSI. The post-processing algorithms successfully removed the water signal and its sidebands without affecting metabolite signals. The best water removal performance was achieved by Löwner tensor-based BSS. Absolute tissue concentrations of citrate in the peripheral zone derived from water-suppressed and unsuppressed 1 H MRSI were the same and as expected from the known physiology of the healthy prostate. Maps for citrate and choline from water-unsuppressed 3D 1 H-MRSI of the prostate showed expected spatial variations in metabolite levels. We developed a robust relatively simple post-processing method of water-unsuppressed MRSI of the prostate to remove the water signal. Absolute quantification using the water signal, originating from the same location as the metabolite signals, avoids the acquisition of additional reference data.

Interesting Features Finder: A New Approach to Multispectral Image Analysis

In this paper, we discuss a new approach to the analysis of multi/hyper-spectral data sets, based on the Interesting Features Finder (IFF) method. The IFF is a simple algorithm recently proposed in the framework of Laser-Induced Breakdown Spectroscopy (LIBS) spectral analysis for detecting ‘interesting’ spectral features independently of the variance they represent in a set of spectra. To test the usefulness of this method to multispectral analysis, we show in this paper the results of its application on the recovery of a ‘lost’ painting from the Etruscan hypogeal tomb of the Volumni (3rd century BCE—1st century CE) in Perugia, Italy. The results obtained applying the IFF algorithm are compared with the results obtained by applying Blind Source Separation (BSS) techniques and Self-Organized Maps (SOM) to a multispectral set of 17 fluorescence and reflection images. From this comparison emerges the possibility of using the IFF algorithm to obtain rapidly and simultaneously, by varying a single parameter in a range from 0 to 1, several sets of elaborated images all containing the ‘interesting’ features and carrying information comparable to what could have been obtained by BSS and SOM, respectively.