Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness
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Kenji Leibnitz | Masayuki Murata | Frank Rattay | Martin Kronbichler | Betty Wutzl | Stefan Martin Golaszewski | M. Kronbichler | F. Rattay | S. Golaszewski | M. Murata | K. Leibnitz | Betty Wutzl
[1] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[2] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[3] J. Giacino,et al. The minimally conscious state: Definition and diagnostic criteria , 2002, Neurology.
[4] A. Poydasheva,et al. Misdiagnosis in doc patients: Russian experience , 2017, Journal of the Neurological Sciences.
[5] Á. Pascual-Leone,et al. A human brain network derived from coma-causing brainstem lesions , 2016, Neurology.
[6] Afrouz Anderson,et al. Relative brain signature: a population-based feature extraction procedure to identify functional biomarkers in the brain of alcoholics , 2015, Brain and behavior.
[7] Steven Laureys,et al. Sleep in the unresponsive wakefulness syndrome and minimally conscious state. , 2013, Journal of neurotrauma.
[8] Susan L. Whitfield-Gabrieli,et al. Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..
[9] B. Caffo,et al. Resting brain activity in disorders of consciousness , 2015, Neurology.
[10] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[11] Yvonne Höller,et al. Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness , 2013, PloS one.
[12] T Sarraf,et al. Long-term outcomes of chronic minimally conscious and vegetative states , 2010, Neurology.
[13] Karl J. Friston,et al. Slice-timing effects and their correction in functional MRI , 2011, NeuroImage.
[14] Lizette Heine,et al. A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network , 2018, Front. Neurol..
[15] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[16] Fred Plum,et al. [The diagnosis of stupor and coma]. , 2015, Brain and nerve = Shinkei kenkyu no shinpo.
[17] Carolina Colomer,et al. Behavioral recovery in disorders of consciousness: a prospective study with the Spanish version of the Coma Recovery Scale-Revised. , 2012, Archives of physical medicine and rehabilitation.
[18] Nicco Reggente,et al. Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning , 2017, Human brain mapping.
[19] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[20] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[21] A. Karantanas,et al. Outcome of patients with diffuse axonal injury: the significance and prognostic value of MRI in the acute phase. , 2000, The Journal of trauma.
[22] L. Hochberg,et al. Early detection of consciousness in patients with acute severe traumatic brain injury , 2017, Brain : a journal of neurology.
[23] Steven Laureys,et al. From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness , 2011, Journal of Neurology.
[24] Lizette Heine,et al. The repetition of behavioral assessments in diagnosis of disorders of consciousness , 2017, Annals of neurology.
[25] G. Tononi,et al. Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. , 2011, Brain : a journal of neurology.
[26] Brian L. Edlow,et al. Functional MRI Motor Imagery Tasks to Detect Command Following in Traumatic Disorders of Consciousness , 2017, Front. Neurol..
[27] Monika Schönauer,et al. Night sleep in patients with vegetative state , 2017, Journal of sleep research.
[28] Dimitri Van De Ville,et al. T114. Predicting coma outcome using resting-state fMRI and machine learning , 2018, Clinical Neurophysiology.
[29] K. Andrews,et al. Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit , 1996, BMJ.
[30] Bernhard Schölkopf,et al. Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.
[31] Dimitri Van De Ville,et al. Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience , 2013, IEEE Signal Processing Magazine.
[32] J. Giacino,et al. The vegetative and minimally conscious states: diagnosis, prognosis and treatment. , 2011, Neurologic clinics.
[33] Aixia Guo,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2014 .
[34] Matthew H. Davis,et al. Detecting Awareness in the Vegetative State , 2006, Science.
[35] Tom M. Mitchell,et al. Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.
[36] Cheng-Lung Huang,et al. A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..
[37] F Segovia,et al. Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease. , 2016, Current Alzheimer research.
[38] Steven Laureys,et al. Disorders of consciousness: responding to requests for novel diagnostic and therapeutic interventions , 2012, The Lancet Neurology.
[39] Steven Laureys,et al. The Role of Neuroimaging Techniques in Establishing Diagnosis, Prognosis and Therapy in Disorders of Consciousness , 2016, The open neuroimaging journal.
[40] M. Boly,et al. Tracking the recovery of consciousness from coma. , 2006, The Journal of clinical investigation.
[41] E. Trinka,et al. The locked-in plus syndrome , 2013, Journal of the Neurological Sciences.
[42] Fernando Pérez-Cruz,et al. Feature Selection via Genetic Optimization , 2002, ICANN.
[43] Stefan Golaszewski,et al. Prediction of recovery from post-traumatic vegetative state with cerebral magnetic-resonance imaging , 1998, The Lancet.
[44] J. Giacino,et al. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. , 2004, Archives of physical medicine and rehabilitation.
[45] Walter G Sannita,et al. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome , 2010, BMC medicine.
[46] Caroline Schnakers,et al. Behavioral assessment in patients with disorders of consciousness: gold standard or fool's gold? , 2009, Progress in brain research.
[47] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[48] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[49] Matilde Leonardi,et al. Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness , 2017, Clinical Neurophysiology.
[50] Vinod Kumar,et al. Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification , 2013, Journal of Digital Imaging.
[51] Olivia Gosseries,et al. Recent advances in disorders of consciousness: Focus on the diagnosis , 2014, Brain injury.
[52] M. Boly,et al. Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment , 2009, BMC neurology.
[53] Klaus-Robert Müller,et al. Introduction to machine learning for brain imaging , 2011, NeuroImage.
[54] Takaya Saito,et al. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.