Blind Source Separation Method Research Articles

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

Autoregressive Moving Average Jointly-Diagonalizable Spatial Covariance Analysis for Joint Source Separation and Dereverberation

This paper describes a computationally-efficient statistical approach to joint (semi-)blind source separation and dereverberation for multichannel noisy reverberant mixture signals. A standard approach to source separation is to formulate a generative model of a multichannel mixture spectrogram that consists of source and spatial models representing the time-frequency power spectral densities (PSDs) and spatial covariance matrices (SCMs) of source images, respectively, and find the maximum-likelihood estimates of these parameters. A state-of-the-art blind source separation method in this thread of research is fast multichannel nonnegative matrix factorization (FastMNMF) based on the low-rank PSDs and jointly-diagonalizable full-rank SCMs. To perform mutually-dependent separation and dereverberation jointly, in this paper we integrate both moving average (MA) and autoregressive (AR) models that represent the early reflections and late reverberations of sources, respectively, into the FastMNMF formalism. Using a pretrained deep generative model of speech PSDs as a source model, we realize semi-blind joint speech separation and dereverberation. We derive an iterative optimization algorithm based on iterative projection or iterative source steering for jointly and efficiently updating the AR parameters and the SCMs. Our experimental results showed the superiority of the proposed ARMA extension over its AR- or MA-ablated version in a speech separation and/or dereverberation task.

Multi-Electromagnetic Jamming Countermeasure for Airborne SAR Based on Maximum SNR Blind Source Separation

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.

Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. With the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. The proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. With the proposed deep CNN, we could potentially build a neural-drive-based human-machine interface that is generalizable to different contraction tasks without retraining.

Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation

This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) from a unified point of view. The common way of deriving such an extension is to replace the multivariate complex Gaussian distribution in the likelihood function with its heavy-tailed generalization, e.g., the multivariate complex Student's t and leptokurtic generalized Gaussian distributions, and tailor-make the corresponding parameter optimization algorithm. Using a wider class of heavy-tailed distributions called a Gaussian scale mixture (GSM), i.e., a mixture of Gaussian distributions whose variances are perturbed by positive random scalars called impulse variables, we propose GSM-FastMNMF and develop an expectation-maximization algorithm that works even when the probability density function of the impulse variables have no analytical expressions. We show that existing heavy-tailed FastMNMF extensions are instances of GSM-FastMNMF and derive a new instance based on the generalized hyperbolic distribution that include the normal-inverse Gaussian, Student's t, and Gaussian distributions as the special cases. Our experiments show that the normal-inverse Gaussian FastMNMF outperforms the state-of-the-art FastMNMF extensions and ILRMA model in speech enhancement and separation in terms of the signal-to-distortion ratio.

A Unified Approach for Simultaneous Graph Learning and Blind Separation of Graph Signal Sources

Blind separation of the sources supported by graphs, i.e., graph signals, is a nascent and challenging Blind Source Separation (BSS) problem. In these cases, along with the statistical independence of the sources, additional dependency information can be interpreted from their graph structure, which provides exploiting different Graph Signal Processing (GSP) tools to improve the separation performance. To the best of our knowledge, in these cases, only GraDe and GraphJADE methods have been proposed to exploit the graph dependencies and/or GSP techniques to improve the separation quality rather than the conventional BSS methods. Despite the significant advantages of these graph-based methods, assuming that the underlying graphs are known is a serious drawback, especially in many real-world applications. To address this issue, in this paper, we propose a Unified objective function for GraphJADE with Graph Learning, namely U-GraphJADE-GL, and use the Block Coordinate Descent (BCD) to optimize it. In our proposed method, along with the separation task, the underlying graphs are learned simultaneously in an efficient manner. We compare the performance of the U-GraphJADE-GL with that of the GraDe with Graph Learning (U-GraDe-GL) and the conventional BSS methods in the BSS task and also analyze the GL performance. Besides, as well as the theoretical and experimental convergence analysis, we derive/state the Cramér-Rao bound (CRB) on the estimation of the mixing and unmixing matrices and also on the attainable Interference-to-Source Ratio (ISR) and compare the asymptotic performance of the proposed method with that of the optimal CRB estimators. To investigate the applicability in real applications, the performance of the proposed method is also compared with that of the abovementioned algorithms for denoising the epileptic Electroencephalogram (EEG) signals and also for the audio speech source separation task. The results show the superiority of the proposed algorithm compared to the other methods in the case of BSS of graph signals with unknown graphs.

Mainlobe jamming suppression via improved BSS method for rotated array radar

This study deals with the problem of mainlobe jamming suppression for rotated array radar. The interference becomes spatially nonstationary while the radar array rotates, which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio (SNR) of pulse compression. In this paper, we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal (RAMS) model firstly. Then the corresponding algorithm improved blind source separation (BSS) using the frequency domain of robust principal component analysis (FD-RPCA-BSS) is proposed based on the established rotating model. It can eliminate the influence of the rotating parts and address the problem of loss of SNR. Finally, the measured peak-to-average power ratio (PAPR) of each separated channel is performed to identify the target echo channel among the separated channels. Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation.

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

Extraction of the GVS electrical artifact from EEG recordings of the motor related cortical potential

BackgroundGalvanic vestibular stimulation (GVS) involves the administration of low-amplitude trans-mastoidal current which induces a dense electrical field across the scalp that is difficult to remove from the EEG record. In two proof-of-concept experiments, we designed a paradigm to evaluate functional limb movement, and tested a method of blind source separation to remove the scalp artifact induced by low-amplitude, alternating current GVS to allow measurement of the motor-related cortical response (MRCP) during voluntary movement. New MethodOff-line Extended Infomax Independent Component Analysis (ICA) was applied to the concatenated dataset to identify and remove core characteristics of the artifact induced by a trans-mastoidal current (Experiment 1: 0.01 Hz, 0.2–3 mA; Experiment 2: 0.01 Hz, 0.3–0.4 mA) during finger (Experiments 1 and 2) and foot tapping (Experiment 2). ResultsIn Experiment 1, a GVS-related independent component was identified and successfully removed without compromising measurement of the MRCP. This success was replicated in Experiment 2 which included both finger and foot tapping, and a higher GVS amplitude, which resulted in the identification of additional GVS-related artifacts. Comparison with existing methodsExisting methods of artifact rejection typically use an offline bandpass filter that overlaps with the frequency range of the MRCP. Even when similar ICA-based approaches have been employed, they have been applied during rest rather than active movement, have not been described in sufficient detail to enable replication, and require significant expertise and bespoke software to implement. ConclusionThe ICA-based approach described here provides a relatively simple and accessible means by which MRCPs can be measured during alternating current GVS. This provides opportunity to identify new biomarkers associated with the therapeutic effects of GVS in people with Parkinson’s disease and other disorders of voluntary movement.

Sensor placement optimization of blind source separation in a wireless acoustic sensor network via hybrid descent methods

Blind source separation (BSS) method separates the desired signals from a mixed observed signal by making full use of spatial information. Spatial information refers the fact that the sources originate from different location in space and thus provides the diversity for the separation. Similarly, this diversity can be further enhanced by optimizing the sensor placement as opposed to a fixed location. This paper aims to fill this research gap by proposing a sensor placement optimization strategy to further improve the performance of BSS. As the problem is non-convex in nature, a new hybrid descent optimization method is proposed by embedding a gradient-based method into the genetic algorithm. The proposed method benefits from the robustness of the genetic algorithm and the fast convergence speed of the gradient-based method. Results show that the optimized sensor placement greatly improves the separation performance of the BSS system across the different reverberation times.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which—depending on their extent—may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

The main-lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main-lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main-lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal-to-noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.

Which BSS method separates better the EEG Signals? A comparison of five different algorithms

A very common strategy for rejecting electroencephalographic (EEG) artifacts, includes the decomposition of filtered EEG signals using a Blind Source Separation (BSS) algorithm, the identification and removal of artifactual components and the reconstruction of the cleaned EEG signals. In this pipeline, the performance of the BSS algorithm, which is defined as its ability to separate properly the independent sources (like the EEG from artifactual sources), is very crucial for rejecting most of the artifacts, while maintaining the most part of EEG intact. The overwhelming majority of the published papers uses the extended INFOMAX version of Independent Component Analysis (ICA) for artifact rejection purposes. But is this the most efficient algorithm to separate EEG signals into independent components? This study comes to shed light to the aforementioned question by assessing the performance of the five most common BSS algorithms. The normalized entropy of the brain-related components, their correlation between independent components with the original sources and the amount of the overall mutual information reduction (MIR) achieved by each decomposition were computed in datasets with systematically varying numbers of electrodes (ranging from 19 tο 99), from 26 real human scalp EEG recordings. Additionally, 54 different datasets containing artificially contaminated EEG signals were also examined for the same purpose, on the basis of the Euclidean distance and the correlation, between the generated Independent Components (ICs) and the original vertical and horizontal eye signals, which were used for the contamination. The results support that the Adaptive Mixture ICA was the best performing BSS method.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks.

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

Blind source separation methods applied to evaluate harmonic contribution

SummaryOne of the greatest challenges found in power quality has been, and still is, the establishing of a reliable noninvasive method for quantifying the harmonic contributions of an electric utility and a consumer at the point of common coupling (PCC). This difficulty arises from the fact that the harmonic distortions measured on the PCC are unidentifiable mixtures from both parties. Currently, some work has focused on exploring the use of blind source separation (BSS) techniques to promote the sharing of harmonic responsibilities in a noninvasive way. However, the usage of this methodology is very recent and still requires thorough evaluations, not only from the point of view of the BSS techniques available in the literature, but also from the perspective of the electrical power systems and the nature of their loads. Thus, seeking to contribute to the consolidation of the use of BSS techniques in the sharing of harmonic responsibilities, this work aimed to investigate the impact of the variation of harmonic current sources on the accuracy of BSS methods. In addition, three algorithms suitable for complex‐valued signals were evaluated computationally and experimentally; the results obtained computationally were compared with the results of the method based on the covariance characteristic of the random vectors, and the experimental results was compared to that obtained through an invasive procedure.

Deficit in knee extension strength following anterior cruciate ligament reconstruction is explained by a reduced neural drive to the vasti muscles.

The persistence of quadriceps weakness represents a major concern following anterior cruciate ligament reconstruction (ACLR). The underlying adaptations occurring in the activity of spinal motoneurons are still unexplored. This study examined the discharge patterns of large populations of motor units (MUs) in the vastus lateralis (VL) and vastus medialis muscles following ACLR. Nine ACLR individuals and 10 controls performed unilateral trapezoidal contractions of the knee extensor muscles at 35%, 50% and 70% of the maximal voluntary isometric force (MVIF). High-density surface electromyography (HDsEMG) was used to record the myoelectrical activity of the vasti muscles in both limbs. HDsEMG signals were decomposed with a convolutive blind source separation method and MU properties were extracted and compared between sides and groups. The ACLR group showed a lower MVIF on the reconstructed side compared to the contralateral side (28.1%; P<0.001). This force deficit was accompanied by reduced MU discharge rates (∼21%; P<0.05), lower absolute MU recruitment and derecruitment thresholds (∼22% and ∼22.5%, respectively; P<0.05) and lower input-output gain of motoneurons (27.3%; P=0.009). Deficits in MU discharge rates of the VL and in absolute recruitment and derecruitment thresholds of both vasti MUs were associated with deficits in MVIF (P<0.05). A strong between-side correlation was found for MU discharge rates of the VL of ACLR individuals (P<0.01). There were no significant between-group differences (P>0.05). These results indicate that mid- to long-term strength deficits following ACLR may be attributable to a reduced neural drive to vasti muscles, with potential changes in excitatory and inhibitory synaptic inputs. KEY POINTS: Impaired expression and control of knee extension forces is common after anterior cruciate ligament reconstruction and is related to high risk of a second injury. To provide novel insights into the neural basis of this impairment, the discharge patterns of motor units in the vastus lateralis and vastus medialis were investigated during voluntary force contractions. There was lower knee extensor strength on the reconstructed side with respect to the contralateral side, which was explained by deficits in motor unit discharge rate and an altered motoneuronal input-output gain. Insufficient excitatory inputs to motoneurons and increased inhibitory afferent signals potentially contributed to these alterations. These results further our understanding of the neural underpinnings of quadriceps weakness following anterior cruciate ligament reconstruction and can help to develop effective rehabilitation protocols to regain muscle strength and reduce the risk of a second injury.

Fusion Method to Estimate Heart Rate from Facial Videos Based on RPPG and RBCG.

Remote sensing of vital signs has been developed to improve the measurement environment by using a camera without a skin-contact sensor. The camera-based method is based on two concepts, namely color and motion. The color-based method, remote photoplethysmography (RPPG), measures the color variation of the face generated by reflectance of blood, whereas the motion-based method, remote ballistocardiography (RBCG), measures the subtle motion of the head generated by heartbeat. The main challenge of remote sensing is overcoming the noise of illumination variance and motion artifacts. The studies on remote sensing have focused on the blind source separation (BSS) method for RGB colors or motions of multiple facial points to overcome the noise. However, they have still been limited in their real-world applications. This study hypothesized that BSS-based combining of colors and the motions can improve the accuracy and feasibility of remote sensing in daily life. Thus, this study proposed a fusion method to estimate heart rate based on RPPG and RBCG by the BSS methods such as ensemble averaging (EA), principal component analysis (PCA), and independent component analysis (ICA). The proposed method was verified by comparing it with previous RPPG and RBCG from three datasets according to illumination variance and motion artifacts. The three main contributions of this study are as follows: (1) the proposed method based on RPPG and RBCG improved the remote sensing with the benefits of each measurement; (2) the proposed method was demonstrated by comparing it with previous methods; and (3) the proposed method was tested in various measurement conditions for more practical applications.

Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals.

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.

Embedded intelligent fetal heart rate monitoring system under empirical mode decomposition algorithm and its usage in pregnant women with pregnancy bacterial infection complications and nursing

Abstractthis study was to discuss the application value of the embedded intelligent fetal heart rate monitoring system (EI system) based on empirical mode decomposition (EMD) algorithm in the monitoring of pregnant women with pregnancy bacterial infection complications, aiming to provide a reference basis for timely nursing of pregnancy bacterial infection complications. In this study, the blind source separation method was introduced to denoise the screened intrinsic modal function (IMF) components based on the EI system and the EMD algorithm. The signal to noise ratio (SNR) based on the EMD optimization algorithm was compared with that of other algorithms. Two hundred and sixty‐eight pregnant women in the obstetric clinic were selected as the research objects and divided into an observation group (abnormal fetal heart rate) and a control group (normal fetal heart rate). The pregnant women in both groups accepted the conventional nursing methods, and the EI system was adopted to monitor the fetal heart rate. The probability of abnormal fetal heart monitoring of pregnant women in observation group and control group was analysed. At the same time, the probability of bacterial infection complications was analysed. It was found that the EI system based on EMD algorithm showed obviously higher SNR in contrast to other algorithms. The waveforms of fetal heart monitoring in the observation group and the control group were significantly different (χ2 = 11.215, p &lt; 0.05); microscopic examination of pregnant women secretion in the bacterial infection group revealed the presence of a large number of pathogenic bacteria; it was found that the infection rate of vaginosis was significantly different between the control group and the observation group (p &lt; 0.01); there was a significant difference between the observation group and the control group in the complication of premature rupture of fetal membrane caused by bacteria (χ2 = 11.2, p &lt; 0.01). In conclusion, the EI system based on the EMD algorithm could be undertaken as a tool for the preliminary diagnosis of pregnancy bacterial infection complications in pregnant women, which provided relevant basis for later nursing.

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.