The Experts below are selected from a list of 31338 Experts worldwide ranked by ideXlab platform
Andreas Schulzebonhage - One of the best experts on this subject based on the ideXlab platform.
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automatic 80 250 hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
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automatic 80 250hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
Matthias Dumpelmann - One of the best experts on this subject based on the ideXlab platform.
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automatic 80 250 hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
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automatic 80 250hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
Julia Jacobs - One of the best experts on this subject based on the ideXlab platform.
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automatic 80 250 hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
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automatic 80 250hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
Karolin Kerber - One of the best experts on this subject based on the ideXlab platform.
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automatic 80 250 hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
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automatic 80 250hz ripple high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network
Clinical Neurophysiology, 2012Co-Authors: Matthias Dumpelmann, Julia Jacobs, Karolin Kerber, Andreas SchulzebonhageAbstract:Abstract Objective Recent studies give evidence that high frequency oscillations (HFOs) in the range between 80 Hz and 500 Hz in invasive recordings of epilepsy patients have the potential to serve as reliable markers of epileptogenicity. This study presents an algorithm for automatic HFO detection. Methods The presented HFO Detector uses a radial basis function neural network. Input features of the Detector were energy, line length and instantaneous frequency. Visual marked “ripple” HFOs (80–250 Hz) of 3 patients were used to train the neural network, and a further 8 patients served for the Detector evaluation. Results Detector sensitivity and specificity were 49.1% and 36.3%. The linear and rank correlation between visual and automatic marked “ripple” HFO counts over the channels were significant for all recordings. A Reference Detector based on the line length achieved a sensitivity of 35.4% and a specificity of 46.8%. Conclusions Automatic detections corresponded only partly to visual markings for single events but the relative distribution of brain regions displaying “ripple” HFO activity is reflected by the automated system. Significance The Detector allows the automatic evaluation of brain areas with high HFO frequency, which is of high relevance for the demarcation of the epileptogenic zone.
Buzatu A. - One of the best experts on this subject based on the ideXlab platform.
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Beam test results of IHEP-NDL Low Gain Avalanche Detectors(LGAD)
'Elsevier BV', 2020Co-Authors: Xiao S., Alderweireldt S., Ali S., Allaire C., Agapopoulou C., Atanov N., Ayoub M. K., Barone G., Benchekroun D., Buzatu A.Abstract:To meet the timing resolution requirement of up-coming High Luminosity LHC (HL-LHC), a new Detector based on the Low-Gain Avalanche Detector(LGAD), High-Granularity Timing Detector (HGTD), is under intensive research in ATLAS. Two types of IHEP-NDL LGADs(BV60 and BV170) for this update is being developed by Institute of High Energy Physics (IHEP) of Chinese Academic of Sciences (CAS) cooperated with Novel Device Laboratory (NDL) of Beijing Normal University and they are now under detailed study. These Detectors are tested with $5GeV$ electron beam at DESY. A SiPM Detector is chosen as a Reference Detector to get the timing resolution of LGADs. The fluctuation of time difference between LGAD and SiPM is extracted by fitting with a Gaussian function. Constant fraction discriminator (CFD) method is used to mitigate the effect of time walk. The timing resolution of $41 \pm 1 ps$ and $63 \pm 1 ps$ are obtained for BV60 and BV170 respectively
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Beam test results of IHEP-NDL Low Gain Avalanche Detectors (LGAD)
'Elsevier BV', 2020Co-Authors: Xiao S., Alderweireldt S., Ali S., Allaire C., Agapopoulou C., Atanov N., Barone G., Benchekroun D., Ayoub M.k., Buzatu A.Abstract:To meet the timing resolution requirement of up-coming High Luminosity LHC (HL-LHC), a new Detector based on the Low-Gain Avalanche Detector(LGAD), High-Granularity Timing Detector (HGTD), is under intensive research in ATLAS. Two types of IHEP-NDL LGADs(BV60 and BV170) for this update is being developed by Institute of High Energy Physics (IHEP) of Chinese Academic of Sciences (CAS) cooperated with Novel Device Laboratory (NDL) of Beijing Normal University and they are now under detailed study. These Detectors are tested with $5GeV$ electron beam at DESY. A SiPM Detector is chosen as a Reference Detector to get the timing resolution of LGADs. The fluctuation of time difference between LGAD and SiPM is extracted by fitting with a Gaussian function. Constant fraction discriminator (CFD) method is used to mitigate the effect of time walk. The timing resolution of $41 \pm 1 ps$ and $63 \pm 1 ps$ are obtained for BV60 and BV170 respectively.A High-Granularity Timing Detector (HGTD) is proposed based on the Low-Gain Avalanche Detector (LGAD) for the ATLAS experiment to satisfy the time resolution requirement for the up-coming High Luminosity at LHC (HL-LHC). We report on beam test results for two proto-types LGADs (BV60 and BV170) developed for the HGTD. Such modules were manufactured by the Institute of High Energy Physics (IHEP) of Chinese Academy of Sciences (CAS) collaborated with Novel Device Laboratory (NDL) of the Beijing Normal University. The beam tests were performed with 5 GeV electron beam at DESY. The timing performance of the LGADs was compared to a trigger counter consisting of a quartz bar coupled to a SiPM readout while extracting Reference SiPM by fitting with a Gaussian function. The time resolution was obtained as 41 ps and 63 ps for the BV60 and the BV170, respectively
