Independent Component Regression Research Articles

Increased Resting-State Functional Connectivity in Patients With Autoimmune Addison Disease.

Individuals with autoimmune Addison disease (AAD) take replacement medication for the lack of adrenal-derived glucocorticoid (GC) and mineralocorticoid hormones from diagnosis. The brain is highly sensitive to these hormones, but the consequence of having AAD for brain health has not been widely addressed. The present study compared resting-state functional connectivity (rs-fc) of the brain between individuals with AAD and healthy controls. Fifty-seven patients with AAD (33 female) and 69 healthy controls (39 female), aged 19 to 43 years were scanned with 3-T magnetic resonance imaging (MRI). Independent component and subsequent dual regression analyses revealed that individuals with AAD had stronger rs-fc compared to controls in 3 networks: the bilateral orbitofrontal cortex (OFC), the left medial visual and left posterior default mode network. A higher GC replacement dose was associated with stronger rs-fc in a small part of the left OFC in patients. We did not find any clear associations between rs-fc and executive functions or mental fatigue. Our results suggest that having AAD affects the baseline functional organization of the brain and that current treatment strategies of AAD may be one risk factor.

Fault detection and diagnosis in a non‐Gaussian process with modified kernel independent component regression

AbstractQuality‐related fault detection and diagnosis are crucial in the data‐driven process monitoring field. Most existing methods are based on principal component analysis (PCA) or partial least squares (PLS), which will miss high‐order statistical information when the industrial process does not satisfy a Gaussian distribution. Meanwhile, the traditional contribution plot is difficult to directly apply to nonlinear processes in some cases due to its limitation of convergence. As such, a modified kernel independent component regression (MKICR) model, which considers high‐order statistical information, is proposed for quality‐related fault detection and faulty variable identification. First, the relationship between the independent components and quality variables is established by kernel independent component regression, and the correlation matrix is obtained. Then, the kernel independent components can be suitably divided into quality‐related and quality‐unrelated parts. Finally, an analysis of the contribution of each variable to the statistics based on Lagrange's mean value theorem is presented. In addition, a numerical case and the Tennessee Eastman process (TEP) demonstrate the efficacy and superiority of the proposed method.

A novel quality-relevant fault detection method based on MICA-SOM multi-subspace partitioning for non-Gaussian industrial processes

BackgroundEnsuring safe production and process quality is important for chemical processes. Therefore, a multi-subspace partitioning method based on modified independent component analysis (MICA) and self-organizing map (SOM) is proposed for quality-relevant fault detection. MICA can extract several independent components from the input data, where the mixing matrix implies relationship between independent components and input data. MethodsAccording to the mixing matrix and SOM, the measured variables are classified into quality-relevant variables and quality-irrelevant variables. The quality-relevant variables are mapped into quality-relevant independent component space (Qr-ICS) and the quality-irrelevant independent component space (Qir-ICS) by modified independent component regression and feature decomposition. Then, Qr-ICS is used for quality-relevant fault detection. Quality-irrelevant variables are divided into several quality-irrelevant subspaces based on the correlations among variables. MICA models are established for the quality-irrelevant subspaces. Combined with the Qir-ICS, quality-irrelevant fault detection can be realized. Significant FindingsThe application in the TE process shows that the proposed method has better quality-relevant monitoring reliability and quality-irrelevant monitoring performance than several comparison methods. The application in an actual industrial verifies the advantages and effectiveness of the proposed method over several traditional methods in practical applications.

Monitoring Method of Non-Gaussian Process Based on Fractal Analysis With Kernel Independent Component Regression

Process monitoring is very important for the safety of industrial production processes. The traditional monitoring method based on independent component analysis has the disadvantages as follows. (1) The importance ordering problem of independent components has not been solved; (2) The dynamic problem is not considered. To address these issues, a fractal dimension-based dynamic kernel independent components regression (FD-KICR) method is proposed. The contributions of the proposed method are as follows: (1) The intrinsic dimension of the data is calculated through the improved fractal dimension, and then the number of selected nonlinear ICs (nICs) is determined; (2) The time lags are computed with fractal dimension to effectively describe the dynamic structure of data; (3) By correlating temperature with independent components selection and indirectly monitoring temperature changes through bands of independent components, this method can effectively monitor the safety of the production process. This proposed method is applied to the electrical fused magnesia furnace (EFMF). The experience results show the effectiveness of this method.

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal.

Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

Determination of optimal number of independent components in yield traits in rice

The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

Some theoretical properties of two kurtosis matrices, with application to invariant coordinate selection

Invariant coordinate selection (ICS) is a multivariate statistical method aimed at detecting data structures by means of the simultaneous diagonalization of two scatter matrices. Statistical applications of ICS include cluster analysis, independent component analysis, outlier detection, regression analysis and projection pursuit. Scatter matrices based on fourth-order moments often appear in ICS, partly due to their known asymptotic behaviour. This paper focuses on their theoretical properties, with special emphasis on symmetric distributions, finite mixtures and stochastic processes. Theoretical results highlight both appealing properties and limitations of kurtosis-based ICS as a tool for detecting data structures.

A Feature Extraction Method Using Linear Model Identification of Voltammetric Electronic Tongue

A novel technique of feature extraction for a voltammetric electronic tongue is presented using system identification method with the subsequent synthesis of an equivalent circuit for black tea and then to predict the total theaflavin (TF) content in it. The equivalent circuit parameters for different tea samples are estimated using the current response data obtained from the voltammetric electronic tongue, on which system identification procedure is applied. These identified circuit parameters are then treated as the features of tea samples. The efficacy of the features is corroborated by developing prediction models for TF and comparing the prediction results with reference to TF content in tea. Various regression models such as principal component regression, partial least-squares regression, independent component regression, multilayer feedforward neural network regression, support vector regression, and extreme learning machine (ELM)-based regression models have been evaluated. The proposed feature extraction method performs better when its prediction accuracy was compared with that of the discrete wavelet transform (DWT), a well-established feature extraction method and the neighborhood components analysis (NCA) for regression, and a feature selection method was introduced here for the first time for signal processing of electronic tongue. A significant reduction in the number of features has been obtained in this work over existing feature extraction techniques.

Decentralized dynamic monitoring based on multi-block reorganized subspace integrated with Bayesian inference for plant-wide process

Multi-block subspace monitoring technology has been widely used in the field of plant-wide process to solve the problem of high complexity between variables. However, recent multi-block partition approaches neither consider variable distribution nor comprehensively analyze quality-relevant fault. To solve this problem, a plant-wide process quality monitoring approach based on multi-block reorganized subspace integrated with Bayesian inference (MBRS-BI) is put forward in this paper. Firstly, in view of the distribution characteristics of data, process variables are divided into Gaussian and non-Gaussian subspaces by Jarque-Bera detection method. Subsequently, in the two subspaces, mutual information (MI) is further employed to determine quality-relevant and quality-irrelevant variables so as to obtain quality-relevant and quality-irrelevant sub-blocks. Dynamic principle component analysis (DPCA) and dynamic independent component analysis (DICA) methods are adopted to monitor Gaussian quality-irrelevant and non-Gaussian quality-irrelevant blocks, respectively. For the monitoring of Gaussian quality-relevant and non-Gaussian quality-relevant blocks, we propose total dynamic principle component regression (TDPCR) and total dynamic independent component regression methods (TDICR) methods to focus on analyzing the impact of faults on output quality. Finally, in order to further achieve plant-wide process dynamic monitoring, all corresponding statistical metrics of sub-blocks are reorganized through Bayesian inference (BI). The proposed method (MBRS-BI) is elaborated by the Tennessee-Eastman (TE) process.

ICA-based denoising for ASL perfusion imaging

Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation.72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data.The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data.The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics.

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes.

Rhythmic auditory cues shape neural network recruitment in Parkinson's disease during repetitive motor behavior.

It is well established clinically that rhythmic auditory cues can improve gait and other motor behaviors in Parkinson's disease (PD) and other disorders. However, the neural systems underlying this therapeutic effect are largely unknown. To investigate this question we scanned people with PD and age-matched healthy controls using functional magnetic resonance imaging (fMRI). All subjects performed a rhythmic motor behavior (right hand finger tapping) with and without simultaneous auditory rhythmic cues at two different speeds (1 and 4Hz). We used spatial independent component analysis (ICA) and regression to identify task-related functional connectivity networks and assessed differences between groups in intra- and inter-network connectivity. Overall, the control group showed greater intra-network connectivity in perceptual and motor related networks during motor tapping both with and without rhythmic cues. The PD group showed greater inter-network connectivity between the auditory network and the executive control network, and between the executive control network and the motor/cerebellar network associated with the motor task performance. We interpret our results as indicating that the temporal rhythmic auditory information may assist compensatory mechanisms through network-level effects, reflected in increased interaction between auditory and executive networks that in turn modulate activity in cortico-cerebellar networks.

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

Potentiality of independent component regression in assessment of the peaks responsible for antimicrobial activity of Satureja hortensis L. and Oliveria decumbens Vent. using GC–MS

Satureja hortensis L. and Oliveria decumbens Vent. are used in traditional medicine in the world due to its antifungal, antibacterial and antioxidant properties. In this study, the antimicrobial activities in Satureja hortensis L. and Oliveria decumbens Vent. essential oils against three microbial strains were evaluated by two different methods, including: disk diffusion method and micro-broth dilution assay [with minimal inhibitory concentration (MIC) between 0.025 and 0.500 µL/mL, and minimal lethal concentration (MLC) between 0.050 and 1.000 µL/mL] and qualitative analysis was performed by gas chromatography–mass spectrometry technique (GC–MS). The peaks potentially responsible for the antimicrobial activity in essential oils samples were indicated by some linear multivariate calibration techniques with different preprocessing methods. From the studied techniques, independent component regression (ICR) was preferred to exhibit the potential antimicrobial active compounds in Satureja hortensis L. and Oliveria decumbens Vent. essential oils because of its high repeatability, simplicity, and interpretability of the regression coefficients. Independent components (ICs) can give more chemical explanation than principal components (PCs), because independence is a high-order statistic that is a much stronger condition than orthogonality.

Quality-relevant independent component regression model for virtual sensing application

Independent component regression (ICR) is an efficient method for tackling non-Gaussian problems. In this work, the defects of the conventional ICR are analyzed, and a novel quality-relevant independent component regression (QR-ICR) method based on distance covariance and distance correlation is proposed. QR-ICR extracts independent components (ICs) using a quality-relevant independent component analysis (QR-ICA) algorithm, which simultaneously maximizes the non-Gaussianity of ICs and statistical dependency between ICs and quality variables. Meanwhile, two new types of statistical criteria, called cumulative percent relevance (CPR) and Max-Dependency (Max-Dep), are proposed to rank the order and determine the number of ICs according to their contributions to quality variables. The proposed QR-ICR(CPR) and QR-ICR(Max-Dep) methods were validated through a vinyl acetate monomer production process and a benchmark near-infrared spectral data. The results have demonstrated that the proposed QR-ICR(CPR) and QR-ICR(Max-Dep) provide simpler predictive models and give better prediction performances than PLS, ICR, ICR(CPR), and ICR(Max-Dep).

The two-year progression of structural and functional cerebral MRI in amyotrophic lateral sclerosis.

MRI has emerged as one of several urgently needed candidate disease progression biomarkers for the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), not least due to its unique ability to non-invasively assess structural and functional cerebral pathology. We sought to identify the extent of detectable change in cerebral MRI metrics over a more prolonged period.Analysis of multi-modal MRI data was performed in a cohort of sixteen patients (13 ALS and 3 with primary lateral sclerosis) in whom it was possible to acquire six-monthly images over two years. Structural brain changes were assessed using voxel-based morphometry of grey matter and shape analysis of sub-cortical grey matter structures, tract-based spatial statistics of diffusion tensor imaging (DTI) metrics optimized for longitudinal analysis in the white matter, as well as whole brain voxel-wise statistics of DTI metrics. Changes in resting state functional MRI (rs-fMRI) were investigated via independent component and dual regression analyses of functional connectivity (FC), controlled for confounding effects of grey matter decline. Both linear changes with time and brain changes correlated with revised ALS functional rating score (ALSFRS-R) decline were studied.Widespread and progressive reductions in grey matter were observed in the precentral gyri and posterior cingulate cortex, as well as progressive local atrophy of the thalamus, caudate, and pallidum bilaterally, and right putamen, hippocampus and amygdala. The most prominent DTI tract-based changes were in the superior longitudinal fasciculi and corpus callosum. More widespread areas of DTI changes included the thalami and caudate nuclei, hippocampi and parahippocampal gyri, insular cortices, anterior and posterior cingulate gyri, frontal operculum and cerebellum. FC decreases were noted between the sensorimotor resting state network and the frontal pole, between a network comprising both thalami and an area in the visual cortex, in relation to both time from baseline and ALSFRS-R decline. FC increases between the left primary motor cortex and left fronto-parietal network were seen for both statistical approaches.A longer period of follow-up, though necessarily involving more slowly-progressive cases, demonstrated widespread changes in both grey and white matter structural MRI measures. The mixed picture of regional decreases and increases in FC is compatible with compensatory change, in what should be viewed as a brain-based disease characterised by larger-scale disintegration of motor and frontal projection cerebral networks.

Selective combination in multiple neural networks prediction using independent component regression approach

&lt;p&gt;Biological processes are highly nonlinear in nature and difficult to represent accurately by simple mathematical models. However, this problem can be solved by using neural network. Neural network is a prominent modeling tool especially when it comes to intricate process such as biological process. In this paper, a multiple single hidden layer with ten hidden neurons Feedforward Artificial Neural Network (FANN) was used to model the complex and dynamic relationships between the input (dilution rate, D) and outputs (conversion, y and dimensionless temperature value, θ) for the reactive biological process. Levenberg-Marquardt Backpropagation training method was used. The multiple neural networks predicted outputs were then combined through three different methods which area simple averaging, Principal Component Regression (PCR) and Independent Component Regression (ICR). Multiple neural networks which were created by the bootstrap approach help improved single neural network performance as well as the model robustness for nonlinear process modeling. Comparison was made between the three methods. The result showed that ICR is slightly superior between the three methods especially in noise level 1,2 and 3, however ICR slightly suffer in noise level 4 and 5. This is due to the independent component regression used the latent factors and non-Gaussian distribution of y and θ values for the combination.&lt;/p&gt;&lt;p&gt;Chemical Engineering Research Bulletin 19(2017) 12-19&lt;/p&gt;

Water Demand Modelling Using Independent Component Regression Technique

Water demand modelling is an active field of research. The modelling and forecasting tools are useful to get the estimation of forecasted water demand for different forecast horizons (e.g. 1 h to 10 years) in order to achieve more efficient and sustainable water resources management systems. However, modelling and forecasting of accurate water demand are challenging and difficult tasks. Several issues make the demand forecasting challenging such as the nature and quality of available data, numerous water demand variables, diversity in forecast horizons and geographical differences in modelling catchments. These issues have motivated a number of studies to be conducted to produce better water demand modelling and forecasting tools in order to improve forecast reliability. A variety of techniques have been adopted in water demand forecasting, however, application of independent component regression (ICR) technique has not been investigated yet. Hence, this study explores, for the first time, the use of the ICR technique in medium term urban water demand forecasting. This uses data from the city of Aquidauana, Brazil. It also compares the performance of the developed ICR model with two other commonly modeling methods, principal component regression and multiple linear regression models. It has been found that ICR model perform better than the other two models in modelling water demand with a higher performance indices (i.e. R 2 , RMSE, NSE and MARE) for the independent validation period. The results indicate that the ICR technique has the potential to develop water demand models successfully. The methodology adopted in this paper can be applied to other countries to develop water demand forecasting model.

Soft sensing of non-Gaussian processes using ensemble modified independent component regression

The modified independent component analysis (MICA) has been proposed to tackle some shortcomings which existed in the original ICA iterative procedures and has found wide applications in non-Gaussian data modeling. Motivated by the success of MICA, the modified independent component regression (MICR) method for predicting quality properties of non-Gaussian processes keeps drawing attention within the soft sensing circle. However, the determination logic for non-quadratic functions involved in the iterative procedures of MICA algorithm has always been empirical. Without enough prior knowledge, no theoretical investigation can be carried out to conclusively prove which non-quadratic function is optimal for improving the precision of regression models. The selection of non-quadratic functions is still a challenge that has rarely been attempted. Recognition of this issue activates the current study, which proposes a novel soft sensing approach through taking advantage of ensemble learning strategy. Instead of focusing on a single non-quadratic function, the proposed ensemble MICR (EMICR) method takes all three non-quadratic functions into account and combines multiple base MICR models into an ensemble through assigning different weights. The enhanced soft sensing performance is validated through case studies on three non-Gaussian systems.