Addressing voice recording replications for Parkinson's disease detection

Expert Systems with Applications

Volumn 46, Issue , 2016, Pages 286-292

  Naranjo, Lizbeth ^a   Pérez, Carlos J ^a   Campos Roca, Yolanda ^a   Martín, Jacinto ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Bayesian binary regression; Gibbs sampling; Latent variables; Parkinson's disease; Speech recordings; Voice features

Indexed keywords

BAYESIAN NETWORKS; EXPERT SYSTEMS; NEURODEGENERATIVE DISEASES; PATTERN RECOGNITION SYSTEMS;

BINARY REGRESSION; GIBBS SAMPLING; LATENT VARIABLE; PARKINSON'S DISEASE; SPEECH RECORDING;

SPEECH RECOGNITION;

EID: 84947294038     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.10.034     Document Type: Article

References (38)

1. Albert J., and Chib S. Bayesian analysis of binary and polychotomous response data Journal of the American Statistical Association 88 1993 669 679
2. Baghai-Ravary L., and Beet S.W. Automatic speech signal analysis for clinical diagnosis and assessment of speech disorders. Springer briefs in electrical and computer engineering - speech tecnology 2013 Springer New York
3. Baken R.J., and Orlikoff R.F. Clinical measurement of speech and voice 2nd 2000 Singular Thomson Learning San Diego
4. Breiman L. Random forests Machine Learning 45 2001 5 32
5. Buonaccorsi J.P. Measurement error: models, methods and applications 2010 Chapman and Hall/CRC Boca Raton, Florida
6. Das R. A comparison of multiple classification methods for diagnosis of Parkinson's disease Expert Systems with Applications 37 2010 1568 1572
7. De Rijk M.C., Launer L.J., Berger K., Breteler M.M., Dartigues J.F., and Baldereschi M. Hofman A. Prevalence of Parkinson's disease in Europe: a collaborative study of population-based cohorts Neurology 54 2000 S21 S23
8. Eskidere O., Ertaç F., and Hanilçi C. A comparison of regression methods for remote tracking of Parkinson's disease progression Expert Systems with Applications 39 2012 5523 5528
9. Fernandez-Millan R., Medina-Merodio J.A., Barchino R., Martinez-Herraiz J.J., and Gutierrez-Martinez J.M. A laboratory test expert system for clinical diagnosis support in primary health care Applied Science 5 2015 222 240
10. Gilks W.R., Richarson S., and Spiegelhalter D.J. Markov chain Monte Carlo in practice 1995 Chapman & Hall/CRC London
11. Halldorsson B.V., Bjornsson A.H., Gudmundsson H.T., Birgisson E.O., Ludviksson B.R., and Gudbjornsson B. A clinical decision support system for the diagnosis, fracture risks and treatment of osteoporosis Computational and Mathematical Methods in Medicine 2015 1 7
12. Harel B., Cannizzaro M., and Snyder P.J. Variability in fundamental frequency during speech in prodromal and incipient Parkinson's disease: a longitudinal case study Brain and Cognition 56 2004 24 29
13. Hariharan M., Polat K., and Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease Computer Methods and Programs in Biomedicine 113 2014 904 913
14. Hertrich I., and Ackermann H. Gender-specific vocal dysfunctions in parkinson's disease: electroglottographic and acoustic analyses Annals of Otology, Rhinology and Laryngology 104 1995 197 202
15. Karpievitch Y.V., Hill E.G., Leclerc A.P., Dabney A.R., and Almeida J.S. An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++ PLoS One 4 2009 e7087(1 10)
16. Keating P., and Garellek M. Acoustic analysis of creaky voice Annual meeting of the linguistic society of America 2015
17. Krom G. A cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals Journal of Speech, Language and Hearing Research 36 1993 254 266
18. Lee M.-L. T., Kuo F.C., Whitmore G.A., and Sklar J. Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations Proceedings of the National Academy of Science U.S.A. 97 2000 9834 9839
19. Little M.A., McSharry P.E., Hunter E.J., Spielman J., and Ramig L.O. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease IEEE Transactions on Biomedical Engineering 56 2009 1015 1022
20. Little M.A., McSharry P.E., Roberts S.J., Costello D.A.E., and Moroz I.M. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection BioMedical Engineering OnLine 6 2007 1 19
21. Michaelis D., Gramss T., and Strube H.W. Glottal-to-noise excitation ratio - a new measure for describing pathological voices Acta Acustica united with Acustica 83 1997 700-706(7)
22. Miller N. Communication changes in Parkinson's disease Revista de Logopedia, Foniatría y Audiología 29 2009 37 46
23. Ntzoufras I. Bayesian modeling using WinBUGS. Wiley series in computational statistics 2011 Wiley New Jersey
24. Pérez C.J., Naranjo L., Martín J., and Campos-Roca Y. A latent variable-based Bayesian regression to address recording replication in Parkinson's disease EURASIP Proceedings of the 22nd European signal processing conference (EUSIPCO-2014) 2014 IEEE Lisbon, Portugal 1447 1451
25. Sakar B.E., Isenkul M.E., Sakar C.O., Sertbas A., Gurgen F., and Delil S. Kursun O. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings IEEE Journal of Biomedical and Health Informatics 17 2013 828 834
26. Sakar C.O., and Kursun O. Telediagnosis of Parkinson's disease using measurements of dysphonia Journal of Medical Systems 34 2010 591 599
27. Shue Y.-L., Keating P., Vicenik C., and Yu K. VoiceSauce: A program for voice analysis Energy 1 2010 H1-A1
28. Silva T., Dutra I., Snasel V., and Platos J. T-SPPA trended statistical preprocessing algorithm El-Qawasmeh E. The international conference on digital information processing and communications I 2011 Springer-Verlag 118 131
29. Singla J., Grover D., and Bhandar A. Medical expert systems for diagnosis of various diseases International Journal of Computer Applications 93 2014 36 43
30. Skodda S., Visser W., and Schleger U. Gender-related patterns of dysprodosy in Parkinson disease and correlation between speech variables and motor symptoms Journal of Voice 25 2011 76 82
31. Strobl C., Boulesteix A.L., Zeileis A., and Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution BMC Bioinformatics 8 2007
32. Stroup W.W. Generalized lineal mixed models: modern concepts, methods and applications 2013 Chapman and Hall/CRC Boca Raton, Florida
33. Titze I.R., and Liang H. Comparison of F0 extraction methods for highprecision voice perturbation measurements Journal of Speech, Language and Hearing Research 36 1993 1120 1133
34. Tsanas A., Little M.A., McSharry P.E., and Ramig L.O. Accurate telemonitoring of Parkinson's disease progression by non-invasive speech tests IEEE Transactions Biomedical Engineering 57 2010 884 893
35. Tsanas A., Little M.A., McSharry P.E., and Ramig L.O. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity The Royal Society Interface 8 2011 842 855
36. Tsanas A., Little M.A., McSharry P.E., Spielman J., and Ramig L.O. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease IEEE Transactions on Biomedical Engineering 59 2012 1264 1271
37. Von Orozco-Arroyave J.R., Arias-Londoño J.D., Vargas-Bonilla J.F., and Nöth E. Analysis of speech from people with Parkinson's disease through nonlinear dynamics Drugman T. Dutoit T. Advances in nonlinear speech processing Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science LNAI 7911 2013 Springer-Verlag 112 119
38. Webb A. Statistical pattern recognition 2002 John Wiley and Sons Chichester