Test-retest reliability and feature selection in physiological time series classification

[1]  N. Speybroeck.  Classification and regression trees , 2012, International Journal of Public Health.

[2]  Thomas Philip Runarsson,et al.  Detecting fraudulent whiplash claims by support vector machines , 2010, Biomed. Signal Process. Control..

[3]  黄亚明.  PhysioBank , 2009 .

[4]  Ralf Bousseljot,et al.  Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet , 2009 .

[5]  Eugene Tuv,et al.  Ensemble-Based Variable Selection Using Independent Probes , 2007 .

[6]  S. Sigurdsson,et al.  Reliability of quantitative EEG features , 2007, Clinical Neurophysiology.

[7]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

[8]  Stephan Lau,et al.  Low HRV entropy is strongly associated with myocardial infarction , 2006, Biomedizinische Technik. Biomedical engineering.

[9]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[10]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[11]  Thomas Philip Runarsson,et al.  Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[12]  Domenico Conforti,et al.  Kernel-based Support Vector Machine classifiers for early detection of myocardial infarction , 2005, Optim. Methods Softw..

[13]  James Franklin.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[14]  Mattias Ohlsson,et al.  Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks , 2004, Artif. Intell. Medicine.

[15]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[16]  R. Moe-Nilssen,et al.  Test-retest reliability of trunk accelerometric gait analysis. , 2004, Gait & posture.

[17]  Vladimir Krajca,et al.  Objective Assessment of the Degree of Dementia by Means of EEG , 2003, Neuropsychobiology.

[18]  Ranjan Maitra,et al.  Test‐retest reliability estimation of functional MRI data , 2002, Magnetic resonance in medicine.

[19]  Karsten Sternickel,et al.  Automatic pattern recognition in ECG time series , 2002, Comput. Methods Programs Biomed..

[20]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[21]  G. Rondouin,et al.  Diagnostic value of quantitative EEG in Alzheimer’s disease , 2001, Neurophysiologie Clinique/Clinical Neurophysiology.

[22]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[23]  J. Röschke,et al.  Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. , 1996, Electroencephalography and clinical neurophysiology.

[24]  Ron Kohavi,et al.  Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology , 1995, KDD.

[25]  R. Tibshirani,et al.  An Introduction to the Bootstrap , 1995 .

[26]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[27]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[28]  D. Levy,et al.  An improved method for adjusting the QT interval for heart rate (the Framingham Heart Study) , 1992, The American journal of cardiology.

[29]  L. Tarassenko,et al.  New method of automated sleep quantification , 1992, Medical and Biological Engineering and Computing.

[30]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[31]  D. Singer,et al.  Reproducibility and relation to mean heart rate of heart rate variability in normal subjects and in patients with congestive heart failure secondary to coronary artery disease. , 1991, The American journal of cardiology.

[32]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[33]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[34]  A. Atienza,et al.  Shape based local thresholding for binarization of document images , 2012, Pattern Recognit. Lett..

[35]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[36]  Judith K Sluiter,et al.  Test-retest reliability of heart rate variability and respiration rate at rest and during light physical activity in normal subjects. , 2007, Archives of medical research.

[37]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001, Springer Series in Statistics.

[38]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[40]  R. C. Williamson,et al.  Classification on proximity data with LP-machines , 1999 .

[41]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[42]  R. Meddis.  Statistics Using Ranks: A Unified Approach , 1984 .

[43]  L. Breiman.  Classification and regression trees , 1983 .

[44]  David G. Stork,et al.  Pattern Classification , 1973 .