Abstract
Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis. Nowadays our feature set offers some hopeful conclusions but fails to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce. In this work, the Fractal Dimension (FD) of the observed time series is combined with lineal parameters in the feature vector in order to enhance the performance of the original system.
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References
Mc Kahn, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M.: Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Workgroup on Alzheimer’s disease 24, 939–944 (1984)
McKhann, G.M., 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. Alzheimers Dement 7(3), 263–269 (2011)
Van de Pole, L.A., et al.: The effects of age and Alzheimer’s disease on hippocampal volumes, a MRI study. Alzheimer’s and Dementia, 1(1, suppl. 1), 51 (2005)
Morris, J.C.: The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412b–2414b (1993)
American Psychiatric Association, Diagnostic and Statistical Manual of Mental disorders, 4th Edition Text Revision, Washington DC (2000)
Alzheimer’s Association, http://www.alz.org/
Faundez-Zanuy, M., et al.: Biometric Applications Related to Human Beings: There Is Life beyond Security. Cognitive Computation (2012), doi:10.1007/s12559-012-9169-9
López de Ipiña, K., Alonso, J.B., Solé-Casals, J., Barroso, N., Faundez, M., Ecay, M., Travieso, C., Ezeiza, A., Estanga, A.: Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis. In: Proceedings of NCTA 2012, Barcelona (2012)
Pickover, C.A., Khorasani, A.: Fractal characterization of speech waveform graphs. Comput. Graph. 10(1), 51–61 (1986)
Martinez, F., Guillamon, A., Martinez, J.: Vowel and consonant characterization using fractal dimension in natural speech. In: Proceedings of NOLISP 2003 (2003)
Langi, A., Kinsner, W.: Consonant Characterization Using Correlation Fractal Dimension for Speech Recognition. In: Proceedings of Communications, Power, and Computing. Conference Proceedings. IEEE (1995)
Nelwamondo, F.V., Mahola, U., Marwola, T.: Multi-Scale Fractal Dimension for Speaker Identification Systems. WSEAS Trans. Syst. 5(5), 1152–1157 (2006)
Li, Y., Fan, Y., Tong, Q.: Endpoint Detection In Noisy Environment Using Complexity Measure. In: Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China (2007)
Chen, X., Zhao, H.: Fractal Characteristic-Based Endpoint Detection for Whispered Speech. In: Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing, Lisbon, Portugal (2006)
Praat: doing Phonetics by Computer, http://www.fon.hum.uva.nl/praat
Voice Activity Detector algorithm (VAD), http://www.mathwork.com
Solé, J., Zaiats, V.: A Non-Linear VAD for Noisy Environment. Cognitive Computation 2(3), 191–198 (2010)
Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D 31277, 283 (1988)
Katz, M.: Fractals and the analysis of waveforms. Comput. Biol. Med. 18(3), 145–156 (1988)
Ezeiza, A., de Ipiña, K.L., Hernández, C., Barroso, N.: Enhancing the feature extraction process for automatic speech recognition with fractal dimensions. Cognitive Computation (2012)
Picard, R., Cook, D.: Cross-Validation of Regression Models. Journal of the American Statistical Association 79(387), 575–583 (1984), doi:10.2307/2288403. JSTOR 2288403
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López-de-Ipiña, K. et al. (2013). Feature Extraction Approach Based on Fractal Dimension for Spontaneous Speech Modelling Oriented to Alzheimer Disease Diagnosis. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_19
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DOI: https://doi.org/10.1007/978-3-642-38847-7_19
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