Independent Component Analysis Algorithm Research Articles

Exploring visuospatial function neuroplasticity in elite speed skaters: a resting-state fMRI independent component analysis.

Limited research has been conducted on the neural mechanisms of visuospatial attention in closed skill sports. This research aimed to delve into the unique visuospatial attention abilities of skaters and elucidate the underlying neural mechanisms. This cross-sectional study employed an expert-novice paradigm, applying a purely data-driven approach to analyze and compare the resting-state networks (RSNs) associated with visuospatial attention in 15 elite skaters and 15 control subjects. From the 38 components identified by independent component analysis (ICA) algorithm, five RSNs were selected, including the dorsal attention network (DAN), left and right fronto-parietal network (FPN), somatomotor network (SMN) and visual network (VIS). Elite skaters exhibited heightened functional connectivity (FC) in the right angular gyrus and left precuneus within DAN, left fusiform gyrus within left FPN, right primary motor cortex within right FPN, left supplementary motor area within SMN, and right primary visual cortex within VIS compared to the control group. Conversely, skaters demonstrated diminished FC in the bilateral superior temporal gyrus within DAN and right prefrontal cortex within the right FPN. Statistical results demonstrated significant differences in RSNs related to visuospatial functions in a wide range of brain regions between elite skaters and controls. We further speculate that these variances could be attributable to alterations in visuospatial abilities resulting from years of devoted skating training. The findings of this study offer novel perspectives on the neural reorganization linked to motor training, contributing to an enriched comprehension of the neuroplasticity changes inherent in prolonged engagement in motor skill development.

Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet

Digital Twin (DT) will transform digital healthcare and push it far beyond expectations. DT creates a virtual representation of a physical object reflecting its current state using real-time converted data. Nowadays, Women’s health is more frequently impacted by cervical cancer, but early detection and rapid treatment are critical factors in the cure of cervical cancer. This paper proposes and implements an automated cervical cancer detection DT framework in healthcare. This framework is a valuable approach to enhance digital healthcare operations. In this proposed work, the SIPaKMeD dataset was used for multi-cell classification. There were 1013 images (Input size 224 × 224 × 3) in the collection, from which 4103 cells could be extracted. As a result, the CervixNet classifier model is developed using machine learning to detect cervical problems and diagnose cervical disease. Using pre-trained recurrent neural networks (RNNs), CervixNet extracted 1172 features, and after that, 792 features were selected using an independent principal component analysis (PCA) algorithm. The implemented models achieved the highest accuracy for predicting cervical cancer using different algorithms. The collected information has shown that integrating DT with the healthcare industry will enhance healthcare procedures by integrating patients and medical staff in a scalable, intelligent, and comprehensive health ecosystem. Finally, the suggested method produces an impressive 98.91 % classification accuracy in all classes, especially for support vector machines (SVM).

Abnormality in Peripheral and Brain Iron Contents and the Relationship with Grey Matter Volumes in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18-35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of "MCCA+jICA" showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the "MCCA+jICA" analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from "MCCA+jICA" analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV.

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.

Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network

Autism Spectrum Disorder (ASD) represents a growing challenge in public health, characterized by irreversible neurodevelopmental abnormalities. Despite a rising prevalence, the underlying aetiology and neural substrates of ASD remain incompletely understood. Resting state-functional Magnetic Resonance Imaging (rs-fMRI) has emerged as a valuable non-invasive tool for probing the organization and cognitive functions of the brain by capturing hemodynamic changes. This study explores the relationship between Functional Connectivity (FC) alterations, measured by rs-fMRI, and the manifestation of ASD-related brain disorders. In this paper, we propose a hybrid approach employing an InfoMax Independent Component Analysis (ICA) algorithm and Artificial Neural Network to distinguish between the ASD subjects and Healthy Controls (HCs). Initially, rs-fMRI datasets are preprocessed using Statistical Parametric Mapping (SPM). Further, these datasets are processed using InfoMax ICA to extract the distinct features such as fractional Amplitude of Low-Frequency Fluctuations (fALFF) and Dynamic Range and visualize the FC alterations by region mapping. In addition to this, the number of activated voxels for each brain regions are estimated to observe the neural abnormalities. Finally, the extracted features are fed as input to train, test and validate the ANN classifier. The efficacy of the proposed network is evaluated using the Autism Brain Imaging Data Exchange (ABIDE) dataset and an accuracy of 75.3 percentage is achieved.

Orthogonal extended infomax algorithm.

Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

Resting-state brain network remodeling after different nerve reconstruction surgeries: a functional magnetic resonance imaging study in brachial plexus injury rats.

JOURNAL/nrgr/04.03/01300535-202505000-00031/figure1/v/2024-07-28T173839Z/r/image-tiff Distinct brain remodeling has been found after different nerve reconstruction strategies, including motor representation of the affected limb. However, differences among reconstruction strategies at the brain network level have not been elucidated. This study aimed to explore intra-network changes related to altered peripheral neural pathways after different nerve reconstruction surgeries, including nerve repair, end-to-end nerve transfer, and end-to-side nerve transfer. Sprague-Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight: no nerve repair, grafted nerve repair, phrenic nerve end-to-end transfer, and end-to-side transfer with a graft sutured to the anterior upper trunk. Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery. The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component. Alterations in intra-network resting-state functional connectivity were compared among the groups. Target muscle reinnervation was assessed by behavioral observation (elbow flexion) and electromyography. The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway. Nerve repair was related to enhanced connectivity within the sensorimotor network, while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation. The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer. Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer. Our study revealed important brain networks related to different nerve reconstructions. These networks may be potential targets for enhancing motor recovery.

Proactive Fault Detection in Rotating Machinery using Machine Learning- A Survey

This study presents a survey of approaches for proactive fault detection in rotating machinery, with a focus on the early identification of bearing faults to enhance equipment reliability and operational efficiency. Traditional methods, relying on physical sensors and manual inspections, often lack the ability to provide timely insights into emerging faults. In contrast, the surveyed approaches integrate non-contact vibration sensors with advanced machine learning techniques, revolutionizing fault detection capabilities. The study presents a brief overview of the methods used in classification of the rotating machinery defects using the machine learning and recommends a combination of machine learning methods at different stages to overcome the challenges of the traditional methods. The collected vibration signals undergo noise reduction via the Hilbert transform, followed by dimensionality reduction and feature selection using Independent Component Analysis (ICA) and Genetic Algorithms (GA), respectively. The selected features are then employed for fault detection and categorization using Random Forest (RF) and Deep Belief Networks (DBN). The Future work will involve the implementation and evaluation of these approaches in real-world industrial settings to validate their effectiveness and reliability.

High Spatial Resolution OFDR System Based on Independent Component Analysis Algorithm for Long-Range Distributed Strain Measurement

High Spatial Resolution OFDR System Based on Independent Component Analysis Algorithm for Long-Range Distributed Strain Measurement

Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction.

Combined the improved fast independent component analysis (FastICA) algorithm with the singular value decomposition algorithm, a single-channel fetal electrocardiogram (fECG) extraction method is proposed. First, the improved FastICA algorithm is used to estimate the maternal ECG component from a single-channel abdominal signal of pregnant women using an overrelaxation factor. Then, a preliminary estimate of the fECG signal is obtained by subtracting from the single-channel abdominal signal. Subsequently, the singular value decomposition algorithm is used to denoise the preliminarily estimated fECG signal to obtain a high signal-to-noise ratio. In addition, in the singular value decomposition algorithm for fetal arrhythmia, an improved method for constructing the ECG signal reconstruction matrix is proposed. Finally, the fECG extraction experiments on synthetic abdominal signals and actual abdominal signals (data from 49 abdominal channels sourced from DAISY database and the non-invasive fECG database in PhysioNet) are carried out. The experimental results show that the method in this paper can effectively improve the signal-to-noise ratio and the accuracy of fECG signal extraction, and is suitable for maternal or fetal arrhythmias. Compared with the FastICA algorithm, the signal-to-noise ratio of the fECG signal extracted by the method in this paper is improved by about 5 dB, and the accuracy of fECG extraction in the PhysioNet database can reach 96.54%.

Machine Learning Based Optical Separation of Overlapping Handprints

Abstract Since overlapping handprints often have the potential to become a breakthrough point in criminal cases, extracting and separating overlapping handprints at the scene has become an urgent problem to be solved in criminal technical examination. In this context, this paper proposes a machine learning-based optical separation method for overlapping handprints, which uses SVM to simplify the classification of the features of the training samples and completes the measurement of the features based on the statistical properties of the image grayscale histogram. Using image blind source separation, independent component analysis and pixel iteration algorithm to separate the overlapped image to get two different spatial resolution images, SSIM is chosen to evaluate the quality of the separated image. Then, the target classification recognition and extraction techniques of the method in the paper are analyzed by overlapping handprint optical separation experiments. The results show that compared with the traditional algorithms, the TPR, FPR, overlap degree and accuracy of this paper’s method in dealing with overlapping handprint images with deep fuzzy features are at a minimum of 0.9258, 0.3723, 0.4645, 0.9572, which are all greater than several other algorithms, which proves that the overlapping handprint optical separation method proposed in this paper based on machine learning has obvious advantages.

Selecting methods for a modular EEG pre-processing pipeline: An objective comparison

Electroencephalography (EEG) to study brain functions has become fundamental in many research settings across very different protocols. Indeed, a plethora of processing methods have been developed, for both data preparation (pre-processing) and analysis. While an effect of the pre-processing on the signal is admitted and accepted, there is an increasing effort to better understand to which extent such an influence may affect the analysis results, and which may be the best practices for the correct data pre-processing.Pre-processing procedures include different steps, and each of them may induce modifications affecting the study results. Thus, we analyze the effect of different methodologies at each step and propose quantitative parameters for the choice of the preferable strategy.We illustrate how method selection may affect the quality of EEG signal in an Action Observation and Motor Imagery protocol, using quantitative indices. We analyzed the effect of different strategies for early-stage data preparation; two independent component analysis (ICA) algorithms (SOBI and Extended Infomax) used for artifact removal; and four re-reference approaches (Common averaged reference-CAR, robust-CAR, reference electrode standardization technique – REST, and reference electrode standardization and interpolation technique – RESIT). The effects of the different pipelines were also evaluated through the computation of event-related spectral perturbation (ERSP) of the sensorimotor rhythm. Results showed that signal segmentation significantly affects the cleaning procedure, while comparable results are obtained across ICA approaches. Finally, similar topographical representations were obtained after the application of CAR, REST, and RESIT re-referencing approaches, where rCAR showed the most different ERSP topographical pattern.

Application of independent component analysis-based dimensionality reduction technique in effective information extraction of high-dimensional high-frequency data

Abstract In this paper, after analyzing the existing independent component analysis algorithms, the high-dimensional high-frequency data are preprocessed by whitening, and the objective functions of crag, negative entropy, approximate negative entropy, and mutual information are set. The independent component analysis model is designed to separate the independence between signals by maximizing non-Gaussianity, estimating great likelihood, and minimizing mutual information measures. In order to verify that the dimensionality reduction technique based on independent component analysis can effectively extract high-dimensional, high-frequency data information, it is applied to the industry’s closing index data and financial market data. The results show that the stock price synthesized using the six major independent factors is almost the same as the original stock price trend of DG, and the difference in stock price evaluation is within 5, indicating that the six major independent factors play a decisive role in the stock price trend. The study shows that the dimensionality reduction technique based on independent component analysis can analyze the volatility of stock prices and obtain more effective information from high-dimensional, high-frequency data.

Measuring the accuracy of ICA-based artifact removal from TMS-evoked potentials

BackgroundThe analysis and interpretation of transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) relies on successful cleaning of the artifacts, which typically mask the early (0–30 ms) TEPs. Independent component analysis (ICA) is possibly the single most utilized methodology to clean these signals. ObjectiveICA-based cleaning is reliable provided that the input data are composed of independent components. Differently, in case the underlying components are to some extent dependent, ICA algorithms may yield erroneous estimates of the components, resulting in incorrectly cleaned data. We aim to ascertain whether TEP signals are suited for ICA. MethodsWe present a systematic analysis of how the properties of simulated artifacts imposed on measured artifact-free TEPs affect the ICA results. The variability of the artifact waveform over the recorded trials is varied from deterministic to stochastic. We measure the accuracy of ICA-based cleaning for each level of variability. ResultsOur findings indicate that, when the trial-to-trial variability of an artifact component is small, which can result in dependencies between underlying components, ICA-based cleaning biases towards eliminating also non-artifactual TEP data. We also show that the variability can be measured using the ICA-derived components, which in turn allows us to estimate the cleaning accuracy. ConclusionAs TEP artifacts tend to have small trial-to-trial variability, one should be aware of the possibility of eliminating brain-derived EEG when applying ICA-based cleaning strategies. In practice, after ICA, the artifact component variability can be measured, and it predicts to some extent the cleaning reliability, even when not knowing the clean ground-truth data.

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

A denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm

AbstractEstimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring.

Enhanced independent component analysis and fuzzy C-mean clustering based on novel bat algorithm for noisy image segmentation

Fuzzy c-means clustering is widely recognized as one of the most effective methods for image segmentation and achieving accurate classification. However, this method has two significant drawbacks: its sensitivity to noise and its convergence to local minimum clusters’ centroids. In this paper, we proposed a novel model called EIFCMNB, which incorporates enhanced independent component analysis (EICA), fuzzy c-means clustering (FCMC) and novel bat algorithm (NBA) for noise image segmentation. The suggested model consists of two main phases: image denoising and extraction of the regions of interest (ROIs). In the first phase, the enhanced independent component analysis (EICA) algorithm is used for recovering a good quality image, from a noisy image of poor quality. Several noisy images, with noise variances ranging from 5 to 20, were filtered. The resulting images were then evaluated based on several criteria viz: Peak Signal to Noise Ratio (PSNR), Relative Norm Error (RE), Normalized Cross-Correlation (NCC), and Structural Similarity index measure (SSIM). In the second phase, the fuzzy c-means clustering based on a novel bat algorithm is adopted to calculate optimal clusters’ centroids and extract the ROIs. By incorporating the new bat algorithm, we aim to overcome the problem of converging to local minimums and achieve improved segmentation accuracy. Promising experimental results have been obtained by applying the proposed model to MRI brain images and x-ray welding images. Two criteria viz: VPE end VPC have been employed to evaluate the suggested approach. The experiments clearly demonstrate that our suggested model effectively addresses the sensitivity to noise problem and provides optimal clusters’ centroids. Moreover, it outperforms several FCMC-based algorithms, exhibiting superior performance in terms of image segmentation and classification.

Deep learning with fetal ECG recognition

Objective. Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy. Approach. A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel’s signal is the target FECG. Main results. Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively. Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.

EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM

Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements.