Functional connectivity network based on graph analysis of scalp EEG for epileptic classification

Sargolzaei, Saman; Cabrerizo, Mercedes; Goryawala, Mohammed; Eddin, Anas Salah; Adjouadi, Malek

doi:10.1109/spmb.2013.6736779

Cited by 19 publications

(14 citation statements)

References 22 publications

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“…There are other possibilities for using the proposed encoding method such as: (1) apply graph theory [37] to find the connectivity between encoded spike trains, or (2) build spiking self organizing map and supervised learning systems to further process the encoded spike trains, and classify the patterns represented by the encoded spike trains into meaningful symbols. Since wavelet decomposition is also an important tool in feature extraction step for EEG signal processing [38], WED could facilitate EEG processing with a new online and biological plausible way to extract features from the EEG channels.…”

Section: Discussionmentioning

confidence: 99%

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

2016

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Section: Discussionmentioning

confidence: 99%

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

2016

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“…Anatomical imaging of brain using MRI has been cited amongst the most accurate techniques to detect structural abnormalities that arise in the diseased brain [3]–[9] . The accuracy of image analysis pipelines for functional MRI (fMRI) as well as Diffusion Weighted Imaging (DWI) and associated next level interpretations in the form of connectivity networks [10]–[14], are primarily dependent on a basic step in segmenting the acquired image volume to brain and non-brain tissues to generate a brain-specific mask. Significant advances have been made toward automating this process of brain extraction in the clinical research arena with neuroimaging software packages such as AFNI [15], [16], Freesurfer [17], FSL [18], and SPM [19].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat

Sargolzaei

Cai

Wolahan

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Accurate pre-clinical study reporting requires validated processing tools to increase data reproducibility within and between laboratories. Segmentation of rodent brain from non-brain tissue is an important first step in preclinical imaging pipelines for which well validated tools are still under development. The current study aims to clarify the best approach to automatic brain extraction for studies in the immature rat. Skull stripping modules from AFNI, PCNN-3D, and RATS software packages were assessed for their ability to accurately segment brain from non-brain by comparison to manual segmentation. Comparison was performed using Dice coefficient of similarity. Results showed that the RATS package outperformed the others by including a lower percentage of false positive, non-brain voxels in the brain mask. However, AFNI resulted in a lower percentage of false negative voxels. Although the automatic approaches for brain segmentation significantly facilitate the data stream process, the current study findings suggest that the task of rodent brain segmentation from T2 weighted MRI needs to be accompanied by a supervised quality control step when developmental brain imaging studies were targeted.

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“…There are other possibilities for using the proposed encoding method such as: (1) apply graph theory [92] to find the connectivity between encoded spike trains, or (2) build spiking self-organizing map and supervised learning systems to further process the encoded spike trains, and classify the patterns represented by the encoded spike trains into meaningful symbols. Since wavelet decomposition is also an important tool in feature extraction step for EEG signal processing [93], WSN neurons could facilitate EEG processing with a new online and biologically plausible way to extract features from the EEG channels.…”

Section: Discussionmentioning

confidence: 99%

System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network

Wang¹

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All rights reserved.iv DEDICATION This dissertation is dedicated to the following people:• My wife, Yanyan Pan, who has shared the life uncertainties and challenges during the completion of this dissertation, yet been always patient and supportive of my work.• My parents, Yimin Wang and Wenhua Li, who emphasized the importance of education and always encouraged me even from thousands of miles away.• My son, Lucas Wang, who has grown into a cute one year old boy in spite of his father spending so much time away from him working on this dissertation. Interictal spike (IS) is a type of transient discharge commonly found in the electroencephalography (EEG) records from epilepsy patients. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for essential in seizure prediction and guided therapy. We present in this work a new IS detection technology method using the phase encoding method with customized wavelet sensor neuron and a specially designed ASNN structure. The detection results confirm the ability of such ASNN to capture IS automatically from multichannel EEG records.

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Functional connectivity network based on graph analysis of scalp EEG for epileptic classification

Cited by 19 publications

References 22 publications

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat

System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network

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