Analysis of mean square error surface and its corresponding contour plots of spontaneous speech signals in Alzheimer's disease with adaptive wiener filter

The purpose of this study is to evaluateźhuman speech rate variability and to analyze the dynamics of spontaneous speech signals of two groups, Alzheimer's and healthy subjects, to obtain a detailed understanding of their speech pattern differences so that Alzheimer's disease can be diagnosed automatically and readily. In the approach proposed in this study, the dynamics of the speech signals are analyzed by examining the mean square error (MSE) surface and contour plots quantification of these groups. In general, the results show that the speech signals transit from a high dimensional chaotic state in control subjects to a low dimensional chaotic motion in Alzheimer's patients. This can be due to the decreased interaction of variables in psychological state. Moreover, it can be partially attributed to the fact that in AD the brain begins to shrink, with the number of brain nerve fibers gradually reducing. Spontaneous speech signals are analyzed for Alzheimer's disease diagnosis.Data are collected.Estimation of the mean square error surface of speech for Alzheimer's patients.Using the contour plots quantification of speech during Alzheimer's disease.For the first time this analysis is employed for Alzheimer's disease diagnosis.

[1]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[2]  Gang Sun,et al.  Functional degeneration in dorsal and ventral attention systems in amnestic mild cognitive impairment and Alzheimer’s disease: An fMRI study , 2015, Neuroscience Letters.

[3]  Yudong Zhang,et al.  Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning , 2015, Front. Comput. Neurosci..

[4]  C. Jack,et al.  Biomarker Modeling of Alzheimer’s Disease , 2013, Neuron.

[5]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[6]  Jeny Rajan,et al.  Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering , 2016, Comput. Biol. Medicine.

[7]  S. K. Mullick,et al.  Attractor dimension, entropy and modelling of speech time series , 1990 .

[8]  Steve McLaughlin,et al.  Is speech chaotic?: invariant geometrical measures for speech data , 1994 .

[9]  Adriaan A Lammertsma,et al.  Imaging of neuroinflammation in Alzheimer's disease, multiple sclerosis and stroke: Recent developments in positron emission tomography. , 2016, Biochimica et biophysica acta.

[10]  Jordi Solé i Casals,et al.  Feature Extraction Approach Based on Fractal Dimension for Spontaneous Speech Modelling Oriented to Alzheimer Disease Diagnosis , 2013, NOLISP.

[11]  April Reynolds,et al.  Alzheimer disease: focus on computed tomography. , 2013, Radiologic technology.

[12]  M. Albert,et al.  Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[13]  A.N. Kalinichenko,et al.  Signal stationarity assessment for the heart rate variability spectral analysis , 2008, 2008 Computers in Cardiology.

[14]  K. Kosaka,et al.  Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB) , 1996, Neurology.

[15]  Marco Bozzali,et al.  Brain volumetrics to investigate aging and the principal forms of degenerative cognitive decline: a brief review. , 2008, Magnetic resonance imaging.

[16]  Yudong Zhang,et al.  Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC , 2015, Biomed. Signal Process. Control..

[17]  D Salas-Gonzalez,et al.  Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees , 2010, Physics in medicine and biology.

[18]  Richard J. Kryscio,et al.  Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease , 2014, NeuroImage: Clinical.

[19]  Razali Jidin,et al.  Performance study of adaptive filtering algorithms for noise cancellation of ECG signal , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[20]  Gustavo Deco,et al.  Dynamics extraction in multivariate biomedical time series , 1998, Biological Cybernetics.

[21]  U. Rajendra Acharya,et al.  Application of higher order statistics for atrial arrhythmia classification , 2013, Biomed. Signal Process. Control..

[22]  J. Rohrer.  Structural brain imaging in frontotemporal dementia. , 2012, Biochimica et biophysica acta.

[23]  J. Haddadnia,et al.  Speech Enhancement using Laplacian Mixture Model under Signal Presence Uncertainty , 2014 .

[24]  B. Farhang-Boroujeny,et al.  Adaptive Filters: Theory and Applications , 1999 .

[25]  R. Veerhuis,et al.  Neuroinflammation and regeneration in the early stages of Alzheimer's disease pathology , 2006, International Journal of Developmental Neuroscience.

[26]  José Carlos M. Bermudez,et al.  Statistical analysis of the LMS algorithm with a zero-memory nonlinearity after the adaptive filter , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).