Estimation of Tissue Permeability in MRI Images by Empirical Mode Decomposition Method.

Background: The precise evaluation of tissue permeability using the Magnetic Resonance Imaging (MRI) method requires high-quality images. Due to noisy acquired dynamic MRI images, some methods of processing are required to obtain the imaging detail of interest. Objective: This study aimed to implement Empirical Mode Decomposition (EMD) to the Lock-Locker (LL) images to improve the permeability of the normal tissue and tumor region, evaluated by the Quantitative Autoradiography (QAR) method. Material and Methods: In this experimental and analytical study, the EMD method was used to improve the tissue permeability from the LL- MRI images of the rat brain. The EMD components were extracted from LL images, and the resulting components were combined using different weighting factors. The tissue permeability was derived by extracting the information of each pixel from the LL image series and fitting curves to the data. Results: The optimum weighted combination factors images were 0.7 for the middle and low-frequency components and 1 for the high-frequency component. The calculated tissue permeability was between 0.0023-0.0043 (ml.min-1.g-1) for abnormal tissue. Conclusion: The estimation of the permeability of tumors in the rat brain with the LL images and the processed LL images by the EMD method shows that the EMD method and the weighted combination of frequency components can improve the permeability calculation in the LL images for the rat brain. The results of permeability estimation by EMD due to noise reduction of LL images are closer to the values obtained from the Quantitative Autoradiography (QAR).

Hybrid noise reduction-based data-driven modeling of relative humidity in Khulna, Bangladesh.

In this study, a hybrid Machine Learning (ML) approach is proposed for Relative Humidity (RH) prediction with a combination of Empirical Mode Decomposition (EMD) to improve the prediction accuracy over the traditional prediction technique using a Machine Learning (ML) algorithm called Support Vector Machine (SVM). The main objective of proposing this hybrid technique is to deal with the extremely nonlinear and noisy humidity pattern in Khulna, Bangladesh, which is experiencing rapid urbanization and environmental change. To develop the model, data on temperature, relative humidity, rainfall, and wind speed were collected from the Bangladesh Meteorological Department (BMD), and the data was divided into three phases: 70 % of the historical dataset as training data for the model, 15 % of the data set as the validation phase, and the remaining 15 % of the data set as the test phase of the model. Employing the Particle Swarm Optimization (PSO) algorithm, the SVM model determines its best hypermeters within this research. In the present research, performance analysis is carried out utilizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). Results show that the increase in R2 values resulting from the EMD-based approach is significant: 21.05 % in H1(Traditional model), 19.48 % in H2 (Traditional model), 76.92 % in H3 (Traditional model), 55.93 % in H4 (Traditional model), and 64.29 % in H5 (Traditional model) and H6 (Traditional model). The analytical results show that the proposed EMD-based technique efficiently filters and processes noisy, highly nonlinear humidity data during prediction in the Khulna region. It is recommended that this technique could be applied to other geological areas.

Short-term air quality prediction based on EMD-transformer-BiLSTM.

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015-0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.

Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction.

Wind energy is becoming increasingly competitive, Accurate and reliable multi-engine wind power forecasts can reduce power system operating costs and improve wind power consumption capacity. Existing research on wind power forecasting has neglected the importance of interval forecasting using clusters of wind farms to capture spatial characteristics and the objective selection of forecasting sub-learners, leading to increased uncertainty and risk in system operation. This paper proposes a new "decomposition-aggregation-multi-model parallel prediction" method. The data set is pre-processed by a decomposition-aggregation strategy and spatial feature extraction, and then a Stacking model with multiple parallel sub-learners selected by bootstrap method is used for point and interval forecasting. Experiments and discussions are conducted based on 15-min resolution wind power data from a cluster dataset of a wind farm in northwest China. The experimental results indicate that the method achieves higher accuracy and reliability in both point prediction and interval prediction than other comparative models, with a root mean square error value of 7.47 and an average F value of 1.572, which can provide a reliable reference for power generation planning from wind farm clusters.

Identification of the proteolytic signature in CVB3-infected cells.

Coxsackievirus B3 (CVB3) encodes proteinases that are essential for processing of the translated viral polyprotein. Viral proteinases also target host proteins to manipulate cellular processes and evade innate antiviral responses to promote replication and infection. While some host protein substrates of the CVB3 3C and 2A cysteine proteinases have been identified, the full repertoire of targets is not known. Here, we utilize an unbiased quantitative proteomics-based approach termed terminal amine isotopic labeling of substrates (TAILS) to conduct a global analysis of CVB3 protease-generated N-terminal peptides in both human HeLa and mouse cardiomyocyte (HL-1) cell lines infected with CVB3. We identified >800 proteins that are cleaved in CVB3-infected HeLa and HL-1 cells including the viral polyprotein, known substrates of viral 3C proteinase such as PABP, DDX58, and HNRNPs M, K, and D and novel cellular proteins. Network and GO-term analysis showed an enrichment in biological processes including immune response and activation, RNA processing, and lipid metabolism. We validated a subset of candidate substrates that are cleaved under CVB3 infection and some are direct targets of 3C proteinase in vitro. Moreover, depletion of a subset of TAILS-identified target proteins decreased viral yield. Characterization of two target proteins showed that expression of 3Cpro-targeted cleaved fragments of emerin and aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 modulated autophagy and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, respectively. The comprehensive identification of host proteins targeted during virus infection provides insights into the cellular pathways manipulated to facilitate infection. IMPORTANCE: RNA viruses encode proteases that are responsible for processing viral proteins into their mature form. Viral proteases also target and cleave host cellular proteins; however, the full catalog of these target proteins is incomplete. We use a technique called terminal amine isotopic labeling of substrates (TAILS), an N-terminomics to identify host proteins that are cleaved under virus infection. We identify hundreds of cellular proteins that are cleaved under infection, some of which are targeted directly by viral protease. Revealing these target proteins provides insights into the host cellular pathways and antiviral signaling factors that are modulated to promote virus infection and potentially leading to virus-induced pathogenesis.

Unveiling the Heartbeat of Healing: Exploring Organizational Culture in a Tertiary Hospital's Emergency Medicine Department and Its Influence on Employee Behavior and Well-Being.

This study examined the organizational culture of an emergency medicine department (EMD) in a tertiary hospital in Karnataka, India, using a prospective cross-sectional design from January to February 2024. It aimed to identify the predominant and supporting organizational cultures within the EMD and their influence on employee behavior and well-being, including job satisfaction, burnout, stress levels, and coping strategies. A total of 82 participants, including physicians, emergency medical technicians, and nurses, completed the Organizational Culture Assessment Instrument (OCAI) and a self-designed questionnaire. Ethical clearance was obtained (IEC2-656). Clan culture emerged as the dominant culture (73.17%), emphasizing collaboration and adaptability, correlated with lower stress levels and high job satisfaction (90.78%). Emotional exhaustion was the most common burnout symptom (53.66%). The coping strategies varied, with employees in Clan cultures seeking social support, while those in Hierarchy cultures sought guidance from superiors. This study highlighted the significant role of organization culture in employee well-being and EMD effectiveness, influenced by social values like respect for authority. The limitations included single-setting analysis, an uneven subgroup representation, and a lack of qualitative insights. Future research should involve multiple hospitals and qualitative methods for a comprehensive understanding.

Separation of ventilation and perfusion of electrical impedance tomography image streams using multi-dimensional ensemble empirical mode decomposition.

Objective.In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach.This study analyses the potential ofMulti-dimensional Ensemble Empirical Mode Decompositionby application of theComplete Ensemble Empirical Mode Decomposition with Adaptive Noiseand a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results.In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on theRoot Mean Square Errorbetween the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance.Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

Integration of silicon nanostructures for health and energy applications using MACE: a cost-effective process.

Silicon in its nanoscale range offers a versatile scope in biomedical, photovoltaic, and solar cell applications. Due to its compatibility in integration with complex molecules owing to changes in charge density of as-fabricated Silicon Nanostructures (SiNSs) to realize label-free and real-time detection of certain biological and chemical species with certain biomolecules, it can be exploited as an indicator for ultra-sensitive and cost-effective biosensing applications in disease diagnosis. The morphological changes of SiNSs modified receptors (PNA, DNA, etc) have huge future scope in optimized sensitivity (due to conductance variations of SiNSs) of target biomolecules in health care applications. Further, due to the unique optical and electrical properties of SiNSs realized using the chemical etching technique, they can be used as an indicator for photovoltaic and solar cell applications. In this work, emphasis is given on different critical parameters that control the fabrication morphologies of SiNSs using metal-assisted chemical etching technique (MACE) and its corresponding fabrication mechanisms focusing on numerous applications in energy storage and health care domains. The evolution of MACE as a low-cost, easy process control, reproducibility, and convenient fabrication mechanism makes it a highly reliable-process friendly technique employed in photovoltaic, energy storage, and biomedical fields. Analysis of the experimental fabrication to obtain high aspect ratio SiNSs was carried out using iMAGEJ software to understand the role of surface-to-volume ratio in effective bacterial interfacing. Also, the role of silicon nanomaterials has been discussed as effective anti-bacterial surfaces due to the presence of silver investigated in the post-fabrication energy dispersive x-ray spectroscopy analysis using MACE.

An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition.

Emotion recognition using EEG is a difficult study because the signals' unstable behavior, which is brought on by the brain's complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model's utility in emotional computing.

Injectable Platelet-Rich Fibrin and Advanced Platelet-Rich Fibrin Demonstrate Enhanced Anti-Biofilm Effect Compared to Enamel Matrix Derivatives on Decontaminated Titanium Surfaces.

BACKGROUND: The search for effective antimicrobial agents to mitigate peri-implant infections remains a crucial aspect of implant dentistry. This study aimed to evaluate and compare the antimicrobial efficacy of i-PRF, A-PRF+, and enamel matrix derivative (EMD) on decontaminated rough and smooth titanium (Ti) discs. MATERIALS AND METHODS: Rough and smooth Ti discs were coated with multispecies biofilm and thoroughly debrided using a chitosan-bristled brush. Subsequently, i-PRF, A-PRF+, and EMD were applied. Untreated discs served as control. Residual adherent bacteria present on the treated Ti discs were visualized by SEM and quantified using culture technique, and colony-forming units (CFUs) were measured after 48 h and 7 days. RESULTS: i-PRF demonstrated better antimicrobial effectiveness on both smooth and rough implant surfaces as compared to A-PRF+ and EMD (p < 0.001). In all the experimental groups, smooth Ti discs displayed a greater reduction in microbes compared to rough Ti discs when treated with the biologics. The major reduction in CFU values was determined after seven days. CONCLUSIONS: i-PRF as a regenerative material may also be suitable for decontaminating implant surfaces, which could influence tissue healing and regenerative outcomes positively.

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition.

To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan-Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target's location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

Retina images classification based on 2D empirical mode decomposition and multifractal analysis.

Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.

Reconstructive Approach in Residual Periodontal Pockets with Biofunctionalized Heterografts-A Retrospective Comparison of 12-Month Data from Three Centers.

The regenerative capacity of well-preserved blood clots may be enhanced by biologics like enamel matrix derivative (EMD). This retrospective analysis compares outcomes reported by three centers using different heterografts. Center 1 (C1) treated intrabony defects combining cross-linked high-molecular-weight hyaluronic acid (xHyA) with a xenograft; center 2 (C2) used EMD with an allograft combination to graft a residual pocket. Center 3 (C3) combined xHyA with the placement of a resorbable polymer membrane for defect cover. Clinical parameters, BoP reduction, and radiographically observed defect fill at 12-month examination are reported. The 12-month evaluation yielded significant improvements in PPD and CAL at each center (p < 0.001, respectively). Analyses of Covariance revealed significant improvements in all parameters, and a significantly greater CAL gain was revealed for C2 vs. C1 (p = 0.006). Radiographic defect fill presented significantly higher scores for C2 and C3 vs. C1 (p = 0.003 and = 0.014; C2 vs. C3 p = 1.00). Gingival recession increased in C1 and C3 (p = 1.00), while C2 reported no GR after 12 months (C2:C1 p = 0.002; C2:C3 p = 0.005). BoP tendency and pocket closure rate shared similar rates. Within the limitations of the study, a data comparison indicated that xHyA showed a similar capacity to enhance the regenerative response, as known for EMD. Radiographic follow-up underlined xHyA's unique role in new attachment formation.

Glass Transition Temperatures and Thermal Conductivities of Polybutadiene Crosslinked with Randomly Distributed Sulfur Chains Using Molecular Dynamic Simulation.

The thermal conductivities and glass transition temperatures of polybutadiene crosslinked with randomly distributed sulfur chains having different lengths from mono-sulfur (S1) to octa-sulfur (S8) were investigated. The thermal conductivities of the related models as a function of the heat flux autocorrelation function, applying an equilibrium molecular dynamic (EMD) simulation and the Green-Kubo method, were studied for a wide range of temperatures. The influence of the length of sulfur chains, degree of crosslinking, and molar mass of the crosslinker on the glass transition temperature and final values of thermal conductivities were studied. First, the degree of crosslinking is considered constant for the eight simulation models, from mono-sulfur (S1) to octa-sulfur (S8), while the molar mass of the sulfur is increases. The results show that the thermal conductivities of the crosslinked structure decrease with increasing temperature for each model. Moreover, by increasing the lengths of the sulfur chains and the molar weight of the crosslinker, thermal conductivity increases at a constant temperature. The MD simulation demonstrates that the glass transition temperature and density of the crosslinked structure enhance as the length of the sulfur chains and molar mass of the sulfur increase. Second, the molar weight of sulfur is considered constant in these eight models; therefore, the degree of crosslinking decreases with the increase in the lengths of the sulfur chains. The results show that the thermal conductivities of the crosslinked structure decrease with the increase in the temperature for each model. Moreover, by increasing the lengths of sulfur chains and thus decreasing the degree of crosslinking, the trend in changes in thermal conductivities are almost the same for all of these models, so thermal conductivity is constant for a specific temperature. In addition, the glass transition temperature and density of the crosslinked structure decrease.

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding.

OBJECTIVES: Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. METHODS: EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. RESULTS: The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). CONCLUSIONS: A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of -2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.

Examining the roles of visual imagery and working memory in the retrieval of autobiographical memories using a dual-task paradigm.

The retrieval of autobiographical memories involves the construction of mental representations of past personal events. Many researchers examining the processes underlying memory retrieval argue that visual imagery plays a fundamental role. Other researchers, however, have argued that working memory is an integral component involved in memory retrieval. The goal of this study was to resolve these conflicting arguments by comparing the relative contributions of visual imagery and working memory during the retrieval of autobiographical memories in a dual-task paradigm. While following a moving dot, viewing a dynamic visual noise (DVN), or viewing a blank screen, 95 participants recalled their memories and subsequently rated them on different memory characteristics. The results suggest that inhibiting visual imagery by having participants view DVN merely delayed memory retrieval but did not affect the phenomenological quality of the memories retrieved. Taxations to the working memory by having participants follow a moving dot, on the contrary, resulted in only longer retrieval latencies and no reductions in the specificity, vividness, or the emotional intensity of the memories retrieved. Whereas the role of visual imagery during retrieval is clear, future studies could further examine the role of working memory during retrieval by administering a task that is less difficult or by recruiting a larger sample than this study. The results of this study seem to suggest that both visual imagery and working memory play a role during the retrieval of autobiographical memory, but more research needs to be conducted to determine their exact roles.

Automatic selection of IMFs to denoise the sEMG signals using EMD.

Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples.