Integrated Analysis of fMRI-EEG Data for the Detection of Epilepsy Disorder

Abstract. This study aimed to assess whether brain potentials have significant influences on the relationship between aerobic fitness and cognition. Behavioral and electroencephalographic (EEG) data was collected from 48 young adults when performing a Posner task. Higher aerobic fitness is related to faster reaction times (RTs) along with greater P3 amplitude and shorter P3 latency in the valid trials, after controlling for age and body mass index. Moreover, RTs were selectively related to P3 amplitude rather than P3 latency. Specifically, the bootstrap-based mediation model indicates that P3 amplitude mediates the relationship between fitness level and attention performance. Possible explanations regarding the relationships among aerobic fitness, cognitive performance, and brain potentials are discussed.

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Safety of pirfenidone in patients with idiopathic pulmonary fibrosis (IPF): Integrated analysis of cumulative data from 5 clinical trials

Pneumologie ◽

10.1055/s-0036-1572063 ◽

2016 ◽

Vol 70 (S 01) ◽

Author(s):

U Costabel ◽

L Lancaster ◽

C Albera ◽

WZ Bradford ◽

EA Fagan ◽

...

Keyword(s):

Clinical Trials ◽

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Integrated Analysis ◽

Cumulative Data

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Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

Methods of Information in Medicine ◽

10.1055/s-0038-1635557 ◽

1989 ◽

Vol 28 (03) ◽

pp. 160-167 ◽

Cited By ~ 1

Author(s):

P. Penczek ◽

W. Grochulski

Keyword(s):

Dynamic Behaviour ◽

Joint Probability ◽

Drug Effects ◽

Anticonvulsant Drug ◽

Channel System ◽

Markov Network ◽

Eeg Data ◽

Multi Level ◽

Joint Probability Functions ◽

Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

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Entrectinib in neurotrophic receptor tyrosine kinase fusion-positive (NTRK- fp) non-small cell lung cancer (NSCLC): integrated analysis of patients enrolled in STARTRK-2, STARTRK-1 and ALKA-372-001

10.1055/s-0039-3403355 ◽

2020 ◽

Author(s):

L Paz-Ares ◽

RC Doebel ◽

AF Farago ◽

S Liu ◽

SP Chawla ◽

...

Keyword(s):

Lung Cancer ◽

Tyrosine Kinase ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Receptor Tyrosine Kinase ◽

Small Cell ◽

Integrated Analysis ◽

Small Cell Lung

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An integrated analysis of mRNA-miRNA transcriptome data revealed hub regulatory networks in three genitourinary cancers

BIOCELL ◽

10.32604/biocell.2017.00019 ◽

2018 ◽

Vol 41 (1) ◽

pp. 19-26 ◽

Cited By ~ 7

Author(s):

M. Liu ◽

X. Zhang

Keyword(s):

Regulatory Networks ◽

Integrated Analysis ◽

Transcriptome Data ◽

Genitourinary Cancers

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

2018 ◽

pp. 429-441

Author(s):

M. Jeyanthi ◽

C. Velayutham

Keyword(s):

Nearest Neighbor ◽

Technology Development ◽

Vital Role ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Data Set ◽

Eeg Data ◽

Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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