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.

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.

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>

Single and three phase moving average filter PLLs: Digital controller design recipe

2014 ◽  
Vol 116 ◽  
pp. 276-283 ◽  
Author(s):  
Naji Rajai Nasri Ama ◽  
Wilson Komatsu ◽  
Lourenco Matakas Junior
Keyword(s):  
Controller Design ◽  
Moving Average ◽  
Digital Controller ◽  
Moving Average Filter ◽  
Three Phase ◽  
Average Filter

scholarly journals Closing the Wearable Gap—Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 208 ◽  
Author(s):  
Tony Luczak ◽  
Reuben F. Burch V ◽  
Brian K. Smith ◽  
Daniel W. Carruth ◽  
John Lamberth ◽  
...  
Keyword(s):  
Center Of Pressure ◽  
Moving Average ◽  
Pearson Correlation ◽  
Soft Sensors ◽  
Shear Forces ◽  
Autoregressive Integrated Moving Average ◽  
Pressure Sensitive ◽  
Sensor Orientation ◽  
R Value ◽  
Anterior Posterior

The purpose of this study was to evaluate the use of compressible soft robotic sensors (C-SRS) in determining plantar pressure to infer vertical and shear forces in wearable technology: A ground reaction pressure sock (GRPS). To assess pressure relationships between C-SRS, pressure cells on a BodiTrakTM Vector Plate, and KistlerTM Force Plates, thirteen volunteers performed three repetitions of three different movements: squats, shifting center-of-pressure right to left foot, and shifting toes to heels with C-SRS in both anterior–posterior (A/P) and medial–lateral (M/L) sensor orientations. Pearson correlation coefficient of C-SRS to BodiTrakTM Vector Plate resulted in an average R-value greater than 0.70 in 618/780 (79%) of sensor to cell comparisons. An average R-value greater than 0.90 was seen in C-SRS comparison to KistlerTM Force Plates during shifting right to left. An autoregressive integrated moving average (ARIMA) was conducted to identify and estimate future C-SRS data. No significant differences were seen in sensor orientation. Sensors in the A/P orientation reported a mean R2 value of 0.952 and 0.945 in the M/L sensor orientation, reducing the effectiveness to infer shear forces. Given the high R values, the use of C-SRSs to infer normal pressures appears to make the development of the GRPS feasible.

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

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