REACT: Real-time EEG Analysis for Event Detection

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The REACT technology is used for automatic, real-time detection of neurological events. Our focus in this work is on seizure detection, in both neonates and adults. We present three demonstrators of REACT in clinical, laboratory and ambulatory scenarios.

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REACT: R eal-time E EG A nalysis for Event Dete ct ion Andrey Temko, Robert P. McEvoy , Denis Dwyer, Stephen Faul, Gordon Lightbody, William P. Marnane First AMA-IEEE Medical Technology Conference on Individualized Healthcare, Washington, D.C., 22nd March 2010 Department of Electrical & Electronic Engineering University College Cork, IRELAND

Motivation ,[object Object],[object Object],[object Object],REACT Real-time EEG Analysis for Event Detection Point-of-Care Technology

REACT Technology Smoothing & thresholding Support Vector Machine 55 features (frequency envelope, wavelet, model-based (AR), structural, time-domain, etc.) Filtering, re-sampling, segmentation A. Temko, E. Thomas, G. Boylan, W. Marnane and G. Lightbody, "An SVM-Based System and Its Performance for Detection of Seizures in Neonates” S. Faul, A. Temko and W. Marnane "Age-Independent Seizure Detection” Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ‘09), Minneapolis, September 2009

Demonstrators Bedside EEG Monitoring System Offline analysis for both clinicians & engineers Blackfin DSP Clinical Ambulatory Laboratory

Future Work ,[object Object],[object Object],[object Object],{andreyt, robertmce, stephenf}@eleceng.ucc.ie www.ucc.ie/en/neonatalbrain www.ucc.ie/en/eedsp DSP ΣΣ

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In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.

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One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.

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Abstract The present study investigated the influence of electromagnetic fields, similar to that emitted by Wi-Fi system, on brain activity. Fifteen female and fifteen male subjects performed a short memory task (Wechsler test), both without and with exposure to a 2.4GHz Wi-Fi signal. For each subject, radiation condition and electrode, the amplitude in the frequency domain of the EEG signal was calculated from the recordings of 30 scalp electrodes, using the Fourier transform. The presence of radiation had no effect on the energies of alpha and beta band of male subjects, while it reduced these energies of female subjects, resulting in significantly lower energies, as compared to those of males. Delta and theta band energies did not experience any noteworthy effect from gender, radiation condition and their interaction. Conversely, there was a significant interaction effect (gender x radiation) on the energies of alpha and beta rhythms. Interestingly, this pattern was observed for a number of electrodes, which formed two distinct clusters. one located at right- anterior and the second at occipital brain areas. The present data support the idea that Wi-Fi signal may influence normal physiology through changes in gender related cortical excitability, as reflected by alpha and beta EEG frequencies. :UNIVERSITY OF ATHENS

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REACT: Real-time EEG Analysis for Event Detection

1. REACT: R eal-time E EG A nalysis for Event Dete ct ion Andrey Temko, Robert P. McEvoy , Denis Dwyer, Stephen Faul, Gordon Lightbody, William P. Marnane First AMA-IEEE Medical Technology Conference on Individualized Healthcare, Washington, D.C., 22nd March 2010 Department of Electrical & Electronic Engineering University College Cork, IRELAND

3. REACT Technology Smoothing & thresholding Support Vector Machine 55 features (frequency envelope, wavelet, model-based (AR), structural, time-domain, etc.) Filtering, re-sampling, segmentation A. Temko, E. Thomas, G. Boylan, W. Marnane and G. Lightbody, "An SVM-Based System and Its Performance for Detection of Seizures in Neonates” S. Faul, A. Temko and W. Marnane "Age-Independent Seizure Detection” Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC ‘09), Minneapolis, September 2009

4. Demonstrators Bedside EEG Monitoring System Offline analysis for both clinicians & engineers Blackfin DSP Clinical Ambulatory Laboratory

REACT: Real-time EEG Analysis for Event Detection

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