Analysis of Speech from People with Parkinson's Disease through Nonlinear Dynamics

Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet.

[1]  Kate A. Emerich.  Medical Speech-Language Pathology: A Practitioner's Guide, Alex F. Johnson, Barbara H. Jacobson. Thieme (1997), 712 pp; $79.00 , 1999 .

[2]  Pedro Gómez Vilda,et al.  Methodological issues in the development of automatic systems for voice pathology detection , 2006, Biomed. Signal Process. Control..

[3]  Jesús Francisco Vargas-Bonilla,et al.  Automatic detection of hypernasal speech signals using nonlinear and entropy measurements , 2012, INTERSPEECH.

[4]  Max A. Little,et al.  Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests , 2009, IEEE Transactions on Biomedical Engineering.

[5]  L. Ramig,et al.  The Parkinson larynx: tremor and videostroboscopic findings. , 1996, Journal of voice : official journal of the Voice Foundation.

[6]  A. Giovanni,et al.  Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds. , 1999, Journal of voice : official journal of the Voice Foundation.

[7]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[8]  L. Ramig,et al.  Speech treatment for Parkinson's disease. , 2005, NeuroRehabilitation.

[9]  G. Zocchi,et al.  Local cooperativity mechanism in the DNA melting transition. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Siegfried Piepenbrock,et al.  In vivo myograph measurement of muscle contraction at optimal length , 2007, Biomedical engineering online.

[11]  Max A. Little,et al.  Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007 .

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

[13]  Germán Castellanos-Domínguez,et al.  Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients , 2011, IEEE Transactions on Biomedical Engineering.

[14]  Schuster,et al.  Easily calculable measure for the complexity of spatiotemporal patterns. , 1987, Physical review. A, General physics.

[15]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[16]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[17]  Lu Wang,et al.  Gaussian kernel approximate entropy algorithm for analyzing irregularity of time-series , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[18]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[19]  Bozena Kostek,et al.  Automatic assessment of the motor state of the Parkinson ’ s disease patient – a case study , 2012 .

[20]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Iman H Hewedi,et al.  Diagnostic value of progesterone receptor and p53 expression in uterine smooth muscle tumors , 2012, Diagnostic Pathology.

[22]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .