scholarly journals Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised Moving-Average

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
José L. Ferreira ◽  
Yan Wu ◽  
René M. H. Besseling ◽  
Rolf Lamerichs ◽  
Ronald M. Aarts
Keyword(s):  
Moving Average ◽  
Fmri Data ◽  
Accurate Information ◽  
High Temporal Resolution ◽  
Eeg Signal ◽  
Novel Approach ◽  
Aas Method ◽  
Average Filter ◽  
Artefact Correction

Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.

Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region

2020 ◽  
Vol 65 (6) ◽  
pp. 705-720
Author(s):  
Aarti Sharma ◽  
Jaynendra Kumar Rai ◽  
Ravi Prakash Tewari
Keyword(s):  
Right Hemisphere ◽  
Moving Average ◽  
Pearson Correlation ◽  
Relative Energy ◽  
Automated System ◽  
Temporal Region ◽  
Eeg Signal ◽  
Seizure Prediction ◽  
Prediction Time ◽  
Average Filter

AbstractEpilepsy is characterized by uncontrollable seizure during which consciousness of patient is disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the patients suffering from epilepsy. An automated system for seizure prediction is important for seizure enactment, prevention of sudden unexpected deaths and to avoid seizure related injuries. This paper proposes the prediction of an upcoming seizure by analyzing the 23 channel non-stationary EEG signal. EEG signal is divided into smaller segments to change it into quasi-stationary data using an overlapping moving window. Brain region is marked into four regions namely left hemisphere, right hemisphere, central region and temporal region to identify the epileptogenic region. The epileptogenic region shows significant variations during pre-ictal state in comparison to the other regions. So, seizure prediction is carried out by analyzing EEG signals from this region. Seizure prediction is proposed using features extracted from both time and frequency domain. Relative entropy and relative energy are extracted from wavelet transform and Pearson correlation coefficient is obtained from time domain EEG signal. Extracted features have been smoothened using moving average filter. First order derivative of relative features have been used to normalize the intervariability before deciding the threshold for marking the prediction of seizure. Isolated seizures where pre-ictal duration of more than 1 h is reported has been detected with an accuracy of 92.18% with precursory warning 18 min in advance and seizure confirmation 12 min in advance. An overall accuracy of 83.33% with false positive alarm rate of 0.01/h has been obtained for all seizure cases with average prediction time of 9.9 min.

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2005 ◽  
Vol 127 (2) ◽  
pp. 329-339 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2004 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter which is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal which simulates a single fault in gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their non-repeatability values which were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring as they can adapt to changes in quality of incoming data.

A novel approach to white liquor control in a continuous digester using the Box-Jenkins methodology to predict chip bulk density

TAPPI Journal ◽  
2015 ◽  
Vol 14 (6) ◽  
pp. 395-402
Author(s):  
FLÁVIO MARCELO CORREIA ◽  
JOSÉ VICENTE HALLAK D’ANGELO ◽  
SUELI APARECIDA MINGOTI
Keyword(s):  
Bulk Density ◽  
Moving Average ◽  
Pulp Mill ◽  
Statistical Decision ◽  
White Liquor ◽  
Novel Approach ◽  
Cooking Process ◽  
Eucalyptus Kraft Pulp ◽  
Relevant Variables ◽  
Kraft Cooking

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

scholarly journals One Song, Many Voices: Dementia and The Power of Music

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 775-775
Author(s):  
Debra Sheets ◽  
Stuart MacDonald ◽  
Andre Smith
Keyword(s):  
High School Students ◽  
Social Inclusion ◽  
Positive Impact ◽  
Social Connections ◽  
Social Connection ◽  
School Students ◽  
Choral Singing ◽  
Novel Approach ◽  
Care Partners

Abstract Choral singing is a novel approach to reduce dementia stigma and social isolation while offering participants a sense of purpose, joy and social connection. The pervasiveness of stigma surrounding dementia remains one of the biggest barriers to living life with dignity following a diagnosis (Alzheimer Society of Canada, 2018). This paper examines how a social inclusion model of dementia care involving an intergenerational choir for people living with dementia, their care partners and high school students can reduce stigma and foster social connections. Multiple methodologies are used to investigate the effects of choir participation on cognition, stress levels, social connections, stigma, and quality of life. Results demonstrate the positive impact of choir participation and indicate that this socially inclusive intervention offers an effective, non-pharmacological alternative for older adults living with dementia in the community. Discussion focuses on the importance of instituting meaningful and engaging dementia-friendly activities at the community level.

scholarly journals A Novel Approach for Comparison of Reception Performance in a Technique Test and Small-Sided Games

Sports ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 66
Author(s):  
Arne Sørensen ◽  
Vidar Sørensen ◽  
Terje Dalen
Keyword(s):  
Correlation Coefficient ◽  
Rank Order ◽  
Technical Skills ◽  
Quality Score ◽  
Soccer Players ◽  
Moderate Correlation ◽  
Technical Aspects ◽  
Youth Soccer ◽  
Novel Approach

The purpose of this study was to evaluate the correlation between soccer players’ performance of receptions of passes in tests of both isolated technical skills and more match-realistic situations in small-sided games (SSGs). In addition, this study investigated whether the involvement in SSGs (number of receptions) correlated with the quality of receptions in the respective SSGs. The participants were 13 male outfield youth soccer players from teams in the first division of the regional U18 league. The quality of receptions was scored by educated coaches according to set criteria of performance. Statistical analyses of correlations were determined using Spearman’s rank-order correlation coefficient (rs). The main results were (1) a significant correlation in the quality of ball reception between 4vs1 SSGs and 5vs5 SSGs (rs = −0.61, p < 0.01) and (2) a trend towards moderate correlation between the quality of ball reception using a ball projection machine and 5vs5 SSGs (rs = −0.48, p = 0.10). (3) A significant correlation was found between the number of receptions in 5vs5 SSGs and the quality score of receptions in 5vs5 SSGs (rs = −0.70, p < 0.01). The trend towards moderate correlations between 5vs5 SSGs and the isolated technical reception test could imply the importance of training in the technical aspects of ball reception. Moreover, it seems as though the players with the best reception performance are the players who are most involved in SSGs, that is, having the most receptions.

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