The use of noise information for detection of temporomandibular disorder

[1]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[2]  Gene H. Golub,et al.  Matrix computations , 1983 .

[3]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[4]  Harrison M. Wadsworth.  Handbook of Statistical Methods for Engineers and Scientists , 1990 .

[5]  J. D. Farmer,et al.  Optimal shadowing and noise reduction , 1991 .

[6]  P. Grassberger,et al.  A simple noise-reduction method for real data , 1991 .

[7]  R. Wilding,et al.  A computer analysis of normal human masticatory movements recorded with a sirognathograph. , 1991, Archives of oral biology.

[8]  T. Sauer.  A noise reduction method for signals from nonlinear systems , 1992 .

[9]  S. Dworkin,et al.  Research diagnostic criteria for temporomandibular disorders: review, criteria, examinations and specifications, critique. , 1992, Journal of craniomandibular disorders : facial & oral pain.

[10]  Mark R. Muldoon,et al.  Linear Filters and Non‐Linear Systems , 1992 .

[11]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[12]  C. McNeill.  Temporomandibular disorders : guidelines for classification, assessment, and management , 1993 .

[13]  Schreiber,et al.  Noise reduction in chaotic time-series data: A survey of common methods. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  T. Kuwahara,et al.  Effect of malocclusion on mandibular movement during speech. , 1994, The International journal of prosthodontics.

[15]  T. Kuwahara,et al.  Chewing pattern analysis in TMD patients with unilateral and bilateral internal derangement. , 1995, Cranio : the journal of craniomandibular practice.

[16]  Søren Holdt Jensen,et al.  Reduction of broad-band noise in speech by truncated QSVD , 1995, IEEE Trans. Speech Audio Process..

[17]  L. V. Christensen,et al.  Experimental occlusal interferences. Part V. Mandibular rotations versus hemimandibular translations. , 1995, Journal of oral rehabilitation.

[18]  Yariv Ephraim,et al.  A signal subspace approach for speech enhancement , 1995, IEEE Trans. Speech Audio Process..

[19]  L. V. Christensen,et al.  Experimental occlusal interferences. Part I. A review. , 1995, Journal of oral rehabilitation.

[20]  D. Broomhead,et al.  Signals in chaos: a method for the cancellation of deterministic noise from discrete signals , 1995 .

[21]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[22]  J. Okeson,et al.  Temporomandibular disorders in the medical practice. , 1996, The Journal of family practice.

[23]  J. Okeson.  Orofacial pain : guidelines for assessment, diagnosis, and management , 1996 .

[24]  R. Wilding,et al.  Muscle activity and jaw movements as predictors of chewing performance. , 1997, Journal of orofacial pain.

[25]  Nam C. Phamdo,et al.  Signal/noise KLT based approach for enhancing speech degraded by colored noise , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[26]  Saeed Gazor,et al.  An adaptive KLT approach for speech enhancement , 2001, IEEE Trans. Speech Audio Process..

[27]  Anatoly A. Zhigljavsky,et al.  Analysis of Time Series Structure - SSA and Related Techniques , 2001, Monographs on statistics and applied probability.

[28]  A. Mohammad-Djafari.  Bayesian inference for inverse problems , 2001, physics/0110093.

[29]  V. Moskvina,et al.  An Algorithm Based on Singular Spectrum Analysis for Change-Point Detection , 2003 .

[30]  Benoît Champagne,et al.  Incorporating the human hearing properties in the signal subspace approach for speech enhancement , 2003, IEEE Trans. Speech Audio Process..

[31]  Y. Ephraim,et al.  Extension of the signal subspace speech enhancement approach to colored noise , 2003, IEEE Signal Processing Letters.

[32]  Yi Hu,et al.  A generalized subspace approach for enhancing speech corrupted by colored noise , 2003, IEEE Trans. Speech Audio Process..

[33]  S. L. Bridal,et al.  Singular spectrum analysis applied to backscattered ultrasound signals from in vitro human cancellous bone specimens , 2004, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[34]  A. Tomé,et al.  On the use of clustering and local singular spectrum analysis to remove ocular artifacts from electroencephalograms , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[35]  F. J. Alonso,et al.  Application of singular spectrum analysis to the smoothing of raw kinematic signals. , 2005, Journal of biomechanics.

[36]  Saeid Sanei,et al.  Underdetermined Blind Source Separation of Temporomandibular Joint Sounds , 2006, IEEE Transactions on Biomedical Engineering.

[37]  Saeid Sanei,et al.  A facial pattern recognition approach for detection of temporomandibular disorder , 2007, 2007 15th European Signal Processing Conference.

[38]  S. Sanei,et al.  Detection of Temporomandibular Disorder from Facial Pattern , 2007, 2007 15th International Conference on Digital Signal Processing.

[39]  Anatoly A. Zhigljavsky,et al.  Singular spectrum analysis: methodology and application to economics data , 2009, J. Syst. Sci. Complex..

[40]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[41]  Hossein Hassani,et al.  The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets , 2010 .

[42]  Hossein Hassani,et al.  Singular Spectrum Analysis: Methodology and Comparison , 2021, Journal of Data Science.