Robust decomposition of single-channel intramuscular EMG signals at low force levels
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Dario Farina | Silvia Muceli | Roberto Merletti | Kevin C McGill | Hamid R Marateb | D. Farina | R. Merletti | K. McGill | S. Muceli | H. Marateb
[1] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[2] Constantinos S. Pattichis,et al. Neural network models in EMG diagnosis , 1995 .
[3] Ronald S. Lefever,et al. A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials - Part I: Technique, Theory, and Implementation , 1982, IEEE Transactions on Biomedical Engineering.
[4] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[5] Zeynep Erim,et al. Common drive of motor units in regulation of muscle force , 1994, Trends in Neurosciences.
[6] D. Stashuk,et al. Decomposition‐based quantitative electromyography: Methods and initial normative data in five muscles , 2003, Muscle & nerve.
[7] Donald W. Bouldin,et al. A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Desire L. Massart,et al. Looking for Natural Patterns in Analytical Data, 2. Tracing Local Density with OPTICS , 2002, J. Chem. Inf. Comput. Sci..
[9] D. Stashuk,et al. Automatic decomposition of selective needle-detected myoelectric signals , 1988, IEEE Transactions on Biomedical Engineering.
[10] Hamid Reza Marateb,et al. Estimating the accuracy of EMG decomposition results , 2006 .
[11] Hans-Peter Kriegel,et al. OPTICS-OF: Identifying Local Outliers , 1999, PKDD.
[12] Joshua C. Kline,et al. Decomposition of surface EMG signals. , 2006, Journal of neurophysiology.
[13] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[14] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[15] Witold Pedrycz,et al. Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).
[16] Carlo J De Luca,et al. Decomposition of indwelling EMG signals. , 2008, Journal of applied physiology.
[17] Kevin C. McGill,et al. Knowledge-based Automatic Decomposition of EMG Signals , 2006 .
[18] Peter J. Rousseeuw,et al. Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.
[19] Zeynep Erim,et al. Decomposition of Intramuscular EMG Signals Using a Heuristic Fuzzy Expert System , 2008, IEEE Transactions on Biomedical Engineering.
[20] Kevin C McGill,et al. The innervation and organization of motor units in a series-fibered human muscle: the brachioradialis. , 2010, Journal of applied physiology.
[21] D. Farina,et al. Experimental Analysis of Accuracy in the Identification of Motor Unit Spike Trains From High-Density Surface EMG , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[22] Kevin C. McGill,et al. Automatic Decomposition of the Clinical Electromyogram , 1985, IEEE Transactions on Biomedical Engineering.
[23] Armando Malanda-Trigueros,et al. Automated decomposition of intramuscular electromyographic signals , 2006, IEEE Transactions on Biomedical Engineering.
[24] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD 2000.
[25] Li-Xin Wang,et al. A Course In Fuzzy Systems and Control , 1996 .
[26] Dr. D. Stashuk,et al. Robust supervised classification of motor unit action potentials , 2006, Medical and Biological Engineering and Computing.
[27] D W Stashuk,et al. Decomposition and quantitative analysis of clinical electromyographic signals. , 1999, Medical engineering & physics.
[28] Bert U Kleine,et al. Using two-dimensional spatial information in decomposition of surface EMG signals. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[29] K C McGill,et al. Rigorous a Posteriori Assessment of Accuracy in EMG Decomposition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[30] S Andreassen,et al. Methods for computer-aided measurement of motor unit parameters. , 1987, Electroencephalography and clinical neurophysiology. Supplement.
[31] Roberto Merletti,et al. Electromyography. Physiology, engineering and non invasive applications , 2005 .
[32] K C McGill,et al. Automatic decomposition of multichannel intramuscular EMG signals. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[33] D. Stashuk,et al. Robust method for estimating motor unit firing-pattern statistics , 2007, Medical and Biological Engineering and Computing.
[34] Dirk P. Kroese,et al. Kernel density estimation via diffusion , 2010, 1011.2602.
[35] Alexander Adam,et al. Recruitment order of motor units in human vastus lateralis muscle is maintained during fatiguing contractions. , 2003, Journal of neurophysiology.
[36] D Stashuk,et al. EMG signal decomposition: how can it be accomplished and used? , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[37] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[38] Andrew R. Webb,et al. Statistical Pattern Recognition , 1999 .
[39] Roberto Merletti,et al. Outlier detection in high-density surface electromyographic signals , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[40] Hans-Peter Kriegel,et al. LoOP: local outlier probabilities , 2009, CIKM.
[41] D. Stashuk,et al. Adaptive motor unit action potential clustering using shape and temporal information , 2007, Medical and Biological Engineering and Computing.
[42] Kevin C. McGill,et al. Resolving Superimposed MUAPs Using Particle Swarm Optimization , 2009, IEEE Transactions on Biomedical Engineering.
[43] Ke Zhang,et al. A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data , 2009, PAKDD.
[44] D. Massart,et al. Looking for natural patterns in data: Part 1. Density-based approach , 2001 .
[45] George S. Moschytz,et al. A New Framework and Computer Program for Quantitative EMG Signal Analysis , 1984, IEEE Transactions on Biomedical Engineering.
[46] C.I. Christodoulou,et al. Unsupervised pattern recognition for the classification of EMG signals , 1999, IEEE Transactions on Biomedical Engineering.
[47] Dario Farina,et al. Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search , 2010, IEEE Transactions on Biomedical Engineering.
[48] Anjana Gosain,et al. Improving the performance of fuzzy clustering algorithms through outlier identification , 2009, 2009 IEEE International Conference on Fuzzy Systems.
[49] A J Fuglevand,et al. Estimating the strength of common input to human motoneurons from the cross‐correlogram. , 1992, The Journal of physiology.
[50] H. Hermens,et al. European recommendations for surface electromyography: Results of the SENIAM Project , 1999 .
[51] Kevin C. McGill,et al. EMGLAB: An interactive EMG decomposition program , 2005, Journal of Neuroscience Methods.
[52] Kevin C. McGill,et al. Optimal resolution of superimposed action potentials , 2002, IEEE Transactions on Biomedical Engineering.
[53] Kevin C. McGill,et al. Automatic decomposition of the electromyogram , 1983 .
[54] J. Dunn. Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .
[55] E. Zalewska,et al. Evaluation of MUAP shape irregularity-a new concept of quantification , 1995, IEEE Transactions on Biomedical Engineering.
[56] James C. Bezdek,et al. Some new indexes of cluster validity , 1998, IEEE Trans. Syst. Man Cybern. Part B.
[57] Heinrich Niemann,et al. A Fast-Converging Algorithm for Nonlinear Mapping of High-Dimensional Data to a Plane , 1979, IEEE Transactions on Computers.
[58] Carlo J. De Luca,et al. Physiology and Mathematics of Myoelectric Signals , 1979 .
[59] George S. Moschytz,et al. A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients , 2003, IEEE Transactions on Biomedical Engineering.
[60] Kevin C. McGill,et al. High-Resolution Alignment of Sampled Waveforms , 1984, IEEE Transactions on Biomedical Engineering.
[61] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD '00.
[62] F. Buchthal. Electromyography in the evaluation of muscle diseases. , 1985, Neurologic clinics.