Combination Of Independent Component Analysis Research Articles

A Comparative Model for Blurred Text Detection in Wild Scene Using Independent Component Analysis (ICA) and Enhanced Genetic Algorithm (Using a Bird Approach) with Classifiers

The advent of the study of Scene Text Detection and Recognition has exposed some significant challenges text recognition faces, such as blurred text detection. This study proposes a comparative model for detecting blurred text in wild scenes using independent component analysis (ICA) and enhanced genetic algorithm (E-GA) with support vector machine (SVM) and k-nearest neighbors (KNN) as classifiers. The proposed model aims to improve the accuracy of blurred text detection in challenging environments with complex backgrounds, noise, and illumination variations. The proposed model consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the input image is first preprocessed to remove noise and enhance edges using a median filter and a Sobel filter, respectively. Then, the blurred text regions are extracted using the Laplacian of Gaussian (LoG) filter. In the feature extraction stage, ICA is used to extract independent components from the blurred text regions. The extracted components are then fed into an E-GA-based feature selection algorithm to select the most discriminative features. The E-GA simply fine tunes the selection functionalities of the traditional GA using a bird approach. The selected features are then normalized and fed into the SVM and KNN classifiers. Experimental results on a benchmarking dataset (ICDAR 2019 LSVT) shows that the model outperforms state-of-the-art methods in terms of detection accuracy, precision, recall, and F1-score. The proposed model achieves an overall accuracy of 95.13% for SVM and 88.69% for KNN, which is significantly higher than the already existing methods which for SVM is 93%. In conclusion, the proposed model provides a promising approach for detecting blurred text in wild scenes. The combination of ICA, E-GA, and SVM/KNN classifiers enhances the robustness and accuracy of the detection system, which can be beneficial for a wide range of applications, such as text recognition, document analysis, and security systems.

Automatic Eyeblink and Muscular Artifact Detection and Removal From EEG Signals Using k-Nearest Neighbor Classifier and Long Short-Term Memory Networks

Electroencephalogram (EEG) is often corrupted with artifacts originating from sources such as eyes and muscles. Hybrid artifact removal methods often require human intervention for the adjustment of different parameters. We propose a robust method that can automatically detect and remove eyeblink and muscular artifacts from EEG using a k-nearest neighbor (kNN) classifier and a long short-term memory (LSTM) network. Our method adopts a sliding window of 0.5 s to detect and remove the artifacts from EEG. Features, such as the variance, peak-to-peak amplitude, and average rectified value, are calculated for each EEG segment to identify corrupted segments using the kNN classifier. The kNN classifier detects the presence of artifacts, after which the corresponding EEG window is forwarded to the LSTM network for artifact removal. The LSTM network is trained with the corrupted segments of 0.5 s as input and clean segments of 0.5 s as output. Our method achieved an accuracy of 97.4% in identifying corrupted EEG segments and an average correlation coefficient, structural similarity, signal-to-artifact ratio, and normalized mean squared error of 0.69, 0.76, 1.52 dB, and 0.0013, respectively, in cleaning the EEG. Our results outperformed other hybrid methods reported in the literature based on a combination of ensemble empirical mode decomposition and canonical correlation analysis, a combination of independent component analysis and wavelet decomposition, and tensor decomposition. The mean absolute error of our method is also better in comparison to other methods. Our method can be applied to single and multiple channels and does not require any tuning of parameters.

Altered Spinal Cord Functional Connectivity Associated with Parkinson's Disease Progression.

Parkinson's disease (PD) has traditionally been viewed as an α-synucleinopathy brain pathology. Yet evidence based on postmortem human and animal experimental models indicates that the spinal cord may also be affected. Functional magnetic resonance imaging (fMRI) seems to be a promising candidate to better characterize spinal cord functional organization in PD patients. Resting-state spinal fMRI was performed in 70 PD patients and 24 age-matched healthy controls, the patients being divided into three groups based on their motor symptom severity: PDlow (n=24), PDmed (n=22), and PDadv (n=24) groups. A combination of independent component analysis (ICA) and a seed-based approach was applied. When pooling all participants, the ICA revealed distinct ventral and dorsal components distributed along the rostro-caudal axis. This organization was highly reproducible within subgroups of patients and controls. PD severity, assessed by Unified Parkinson's Disease Rating Scale (UPDRS) scores, was associated with a decrease in spinal functional connectivity (FC). Notably, we observed a reduced intersegmental correlation in PD as compared to controls, the latter being negatively associated with patients' upper-limb UPDRS scores (P=0.0085). This negative association between FC and upper-limb UPDRS scores was significant between adjacent C4-C5 (P=0.015) and C5-C6 (P=0.20) cervical segments, levels associated with upper-limb functions. The present study provides the first evidence of spinal cord FC changes in PD and opens new avenues for the effective diagnosis and therapeutic strategies in PD. This underscores how spinal cord fMRI can serve as a powerful tool to characterize, in vivo, spinal circuits for a variety of neurological diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Independent Component Analysis Filter for Small Vessel Contrast Imaging During Fast Tissue Motion.

Suppressing tissue clutter is an essential step in blood flow estimation and visualization, even when using ultrasound contrast agents. Blind source separation (BSS)-based clutter filter for high-framerate ultrasound imaging has been reported to perform better in tissue clutter suppression than the conventional frequency-based wall filter and nonlinear contrast pulsing schemes. The most notable BSS technique, singular value decomposition (SVD) has shown compelling results in cases of slow tissue motion. However, its performance degrades when the tissue motion is faster than the blood flow speed, conditions that are likely to occur when imaging the small vessels, such as in the myocardium. Independent component analysis (ICA) is another BSS technique that has been implemented as a clutter filter in the spatiotemporal domain. Instead, we propose to implement ICA in the spatial domain where motion should have less impact. In this work, we propose a clutter filter with the combination of SVD and ICA to improve the contrast-to-background ratio (CBR) in cases where tissue velocity is significantly faster than the flow speed. In an in vitro study, the range of fast tissue motion velocity was 5-25 mm/s and the range of flow speed was 1-12 mm/s. Our results show that the combination of ICA and SVD yields 7-10 dB higher CBR than SVD alone, especially in the tissue high-velocity range. The improvement is crucial for cardiac imaging where relatively fast myocardial motions are expected.

Cognitive reserve, neurocognitive performance, and high-order resting-state networks in cognitively unimpaired aging

Cognitive Reserve (CR) is considered a protective factor during the aging process. However, although CR is a multifactorial construct, it has been operationalized in a unitary way (years of formal education or IQ). In the present study, a validated measure to categorize CR holistically (Cognitive Reserve Index Questionnaire) was used to evaluate the resting-state functional connectivity in 77 cognitively unimpaired participants aged 50 years and over with high and low CR, and matched brain global atrophy levels. The connectivity of networks linked to attentional (Dorsal Attention Network -DAN-) and executive (Frontal-Parietal Control Network -FPCN-) processes were evaluated by the combination of Independent Component Analysis and seed-based approaches, since these networks have been proposed as candidates to underlie the protective effect of CR in the aging context. Participants with high CR showed an increase of the connectivity in the FPCN and a decrease in the DAN with respect to the low CR group, correlating with neuropsychological scores and supporting that high CR is related to a better neurocognitive preservation during aging.

Towards a More Theory-Driven BCI Using Source Reconstructed Dynamics of EEG Time-Series

Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI applications have improved significantly with the research in the fields of artificial intelligence (AI) and machine learning (ML), fundamental issues of data-driven training restrict the latency that can be achieved under current BCI paradigms. This work explores the possibility of future BCI using a combination of data-driven and theory-driven methods. In this study, an EEG-BCI dataset from steady-state visually evoked potentials (SSVEPs) is applied, where the SSVEP signals contain, source components from the occipital, parietal and frontal regions of the brain. Source reconstruction is done with the combination of independent component analysis (ICA) and low-resolution electromagnetic tomography analysis (LORETA). This method was able to predict BCI classification labels 5[Formula: see text]s earlier, based on pre-recorded signals from the scalp. The novelty of the current contribution lies in utilizing the source reconstructed EEG time-series for BCI classification, which allows for retention of classification accuracy up to 70% while working with the reduced data dimensionality. Implementation of this algorithm will allow a significant reduction in lag in online BCIs.

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

System for adaptive extraction of non-invasive fetal electrocardiogram

This study aimed to find the most suitable combination of adaptive and non-adaptive methods for extraction of non-invasive fetal electrocardiogram (NI-fECG) using signals recorded from the mother’s abdomen. Among the nine methods considered, the combination of independent component analysis (ICA), fast transversal filter (FTF), and complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) proved to be the most effective for the extraction of fECG from abdominal recordings. This combined method was suitable due to both being effective in extracting fECG and being less computationally complex. Further, so far, FTF and CEEMDAN methods have not been extensively tested for fECG extraction, and in particular, have not been examined as a hybrid method. The ICA-FTF-CEEMDAN hybrid algorithm was tested on two patient databases: Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations (FECGDARHA) and PhysioNet Challenge 2013. The evaluation of the accuracy of fQRS complexes detection was performed using the following parameters: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. The fetal heart rate (fHR) determination accuracy was evaluated using Bland–Altman plots and fHR traces. When testing on the FECGDARHA database, average values of ACC =92.98%, SE =95.33%, PPV =96.4% and F1 =95.86% for detection fQRS were achieved. The error in estimating the fHR was −1.02±7.02 (μ±1.96σ) bpm. When testing on the Challenge 2013 database, average values of ACC =78.47%, SE =82.06%, PPV =87.90% and F1 =84.62% for fQRS detection were achieved, and the error in estimating the fHR was −6.62±10.33 (μ±1.96σ) bpm. In addition, a non-invasive morphological analysis (ST analysis) was performed on the records from the FECGDARHA database, which was accurate in 7 of 12 records with values of μ<0.03 and values of ±1.96σ<0.04.

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction.

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19–93.88%], sensitivity 95.09% [95% confidence interval: 93.68–96.03%], positive predictive value 96.36% [95% confidence interval: 95.05–97.17%] and F1-score 95.69% [95% confidence interval: 94.83–96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44–81.85%], sensitivity 81.79% [95% confidence interval: 76.59–85.43%], positive predictive value 87.16% [95% confidence interval: 81.95–90.35%] and F1-score 84.08% [95% confidence interval: 80.75–86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

Removal of physiological artifacts from simultaneous EEG and fMRI recordings

ObjectiveSimultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don’t remove all other physiological artifacts. MethodsWe propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG. ResultsWe evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts. ConclusionsOur proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods. SignificanceOur proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.

Removal methods of EMG Artifacts from EEG Signals

Electromyography (EMG) is the superposition of motor unit action potential (MUAP) in many muscle fibers in time and space. In real measurement, EMG signals will contaminate Electromyography signals, therefore they bring great difficulties to the qualified analysis and interpretation of EEG signals, and it is a momentous step to remove EMG artifacts from EEG signals. In the recent years, new methods were developed for EEG artifacts removal such as Multivariate Empirical Mode Decomposition and Singular Spectrum Analysis. In particular, some researchers combined the two methods and used their respective advantages to remove artifacts more thoroughly without affecting the EEG signal, such as the combination of Independent Component Analysis and Wavelet Method. In this paper, new methods for muscular artifacts removal from EEG above are discussed. Moreover, traditional methods including signal transform, filtering methods and Blind source separation (BSS) are also reviewed.

Improving the Computational Complexity of Artificial Ultrasound Imaging Using a Combination of Independent Component Analysis and Adaptive Filter

Purpose: The artificial aperture imaging method owns a good contrast in the data recording and imaging process. However, this method is very time consuming that prevents its practical implementation. Materials and Methods: In this paper, the separated waveforms are sent by two elements together, instead of a single element, and the combination of the methods of independent component analysis and adaptive filtering both are used to extract different components in the received echoes. The obtained result illustrates that the imaging is performed in less time, and the computational complexity of this method is declined. Results and Conclusion: The proposed algorithm has been evaluated on two sets of simulated data and experimental data. The results indicate that the proposed method in the point phantom mode is only 1.5% worse in the resolution than the conventional artificial aperture method. Also, from the contrasting viewpoint, the proposed method has made the CR parameter worse by about 1.34dB than the conventional artificial aperture method. These adverse points of resolution and contrast in the proposed method are neglected than the conventional artificial aperture method because of a slight decrease in image quality than the artificial aperture method. However, the proposed method improves the computational complexity by 45% than the conventional artificial aperture method. As a result, it has brought the researchers closer to the practical implementation of artificial aperture imaging.

Non-Invasive Fetal Electrocardiogram Extraction Based on Novel Hybrid Method for Intrapartum ST Segment Analysis

This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditional - gold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method.

Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

This study proposes a novel combination of independent component analysis (ICA) in conjunction with support vector machine (SVM) and denoising autoencoder (DA), for the first time, for removal of eyeblink artefacts from the corrupted electroencephalography (EEG). At first the eyeblink corrupted EEG signals are decomposed into independent components (ICs) using ICA, the corrupted-ICs are then identified using SVM as a classifier. From the corrupted-ICs, the artefacted segment is identified with a second SVM classifier and corrected by the pre-trained DA. Finally, inverse-ICA operation is applied on the remaining ICs and the corrected ICs to obtain the artefact-free EEG signal. The proposed methodology modifies only the portion corrupted with artefacts, and does not alter the uncorrupted part, thereby preserving the neural information in the original EEG. The proposed methodology was implemented to remove eyeblinks from the EEG data collected from the publicly available EEGLab data set. The results reveal that the proposed methodology is superior to the other recently reported methods in terms of the mutual information and average correlation coefficient. Further, the proposed method is automatic and does not require any intervention of the operator, whereas the other methods require intervention of the user.

The changes of brain functional networks in young adult smokers based on independent component analysis.

Intrinsic functional connectivity (FC) networks, including the default mode network (DMN), central executive network (CEN), and salience network (SN), have been implicated in nicotine addiction. However, litter evidence exists about the abnormalities in the three networks in young adult smokers. Forty-eight young adult smokers and 49 age- and gender-matched non-smokers were recruited in the present study. Resting-state functional magnetic resonance imaging (fMRI) data were analyzed by a combination of independent component analysis (ICA) and dual regression to identify potential differences of FC patterns in the DMN, CEN, and SN. Compared to non-smokers, young adult smokers showed enhanced FC of the left posterior cingulate cortex (LPCC), right medial prefrontal cortex (RMPFC) and right precuneus within the DMN network, of the right dorsolateral prefrontal cortex (DLPFC) within the right CEN, and of the left anterior insula (LAI) within the SN. We also found increased FC between the DMN, CEN and key node of the SN (anterior insula, AI). Correlation analysis showed that the increased FC within the networks was significantly correlated with smoking behaviors (pack-years, smoking duration, FTND, first smoking age, and number of cigarettes per day). Our findings may provide additional evidence for conceptualizing the framework of nicotine addiction as a disease of intercommunicating brain networks.

A combined Independent Component Analysis–Neural Network model for forecasting exchange rate variation

The currency market is one of the most efficient markets, making it very difficult to predict future prices. Several studies have sought to develop more accurate models to predict the future exchange rate by analyzing econometric models, developing artificial intelligence models and combining both through the creation of hybrid models. This paper proposes a hybrid model for forecasting the variations of five exchange rates related to the US Dollar: Euro, British Pound, Japanese Yen, Swiss Franc and Canadian Dollar. The proposed model uses Independent Component Analysis (ICA) to deconstruct the series into independent components as well as neural networks (NN) to predict each component. This method differentiates this study from previous works where ICA has been used to extract the noise of time series or used to obtain explanatory variables that are then used in forecasting. The proposed model is then compared to random walk, autoregressive and conditional variance models, neural networks, recurrent neural networks and long–short term memory neural networks. The hypothesis of this study supposes that first deconstructing the exchange rate series and then predicting it separately would produce better forecasts than traditional models. By using the mean squared error and mean absolute percentage error as a measures of performance and Model Confidence Sets to statistically test the superiority of the proposed model, our results showed that this model outperformed the other models examined and significantly improved the accuracy of forecasts. These findings support this model’s use in future research and in decision-making related to investments.

Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation

As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (Pdis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

Trajectory optimization by using EMD- and ICA-based processing method

The angular velocity data of Fiber-Optic Gyro (FOG) are key signals to trajectory calculation for a FOG-based curve measuring system. The shocks from bumps on the route will contaminate the signal and finally drift the calculated trajectory. An optimized processing strategy on the contaminated signals is able to suppress the drifting with almost no trajectory distortion. In this paper, the signals from FOG have been processed by using median filtering (MF), Empirical Mode Decomposition (EMD) and the combined method of EMD and Independent Component Analysis (ICA). The experiments are performed under different conditions. The frequency spectrum and deviation study before and after processing have been investigated to compare the performance of the employed strategies. The combined EMD and ICA method has shown the best performance on trajectory optimization and error suppression with almost no distortion.

Novel Hybrid Extraction Systems for Fetal Heart Rate Variability Monitoring Based on Non-Invasive Fetal Electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.