4.2 Article

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Data classification with binary response through the Boosting algorithm and logistic regression

Fortunato S. de Menezes et al.

EXPERT SYSTEMS WITH APPLICATIONS (2017)

Article Psychology, Experimental

Should metacognition be measured by logistic regression?

Manuel Rausch et al.

CONSCIOUSNESS AND COGNITION (2017)

Article Critical Care Medicine

Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture

Chen-Chiang Lin et al.

INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED (2010)

Article Engineering, Electrical & Electronic

Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

Hasan Ocak

SIGNAL PROCESSING (2008)

Review Psychology, Clinical

EEG biofeedback as a treatment for substance use disorders: Review, rating of efficacy, and recommendations for further research

Tato M. Sokhadze et al.

APPLIED PSYCHOPHYSIOLOGY AND BIOFEEDBACK (2008)

Article Computer Science, Artificial Intelligence

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

Michael Green et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2006)

Article Computer Science, Interdisciplinary Applications

Classification of EEG signals using neural network and logistic regression

A Subasi et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2005)

Article Engineering, Biomedical

Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

D Garrett et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2003)

Review Computer Science, Interdisciplinary Applications

Logistic regression and artificial neural network classification models: a methodology review

S Dreiseitl et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2002)