Clinician's Road Map to Wavelet EEG as an Alzheimer's disease Biomarker

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art_KANDA_Clinician_s_Road_Map_to_Wavelet_EEG_as_2014.PDF (440.13 KB)
Citações na Scopus
20
Tipo de produção
article
Data de publicação
2014
Editora
EEG & CLINICAL NEUROSCIENCE SOC (E C N S)
DOI
Indexadores
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Autores
TRAMBAIOLLI, Lucas R.
LORENA, Ana C.
FRAGA, Francisco J.
Autor de Grupo de pesquisa
Editores
Coordenadores
Organizadores
Citação
CLINICAL EEG AND NEUROSCIENCE, v.45, n.2, p.104-112, 2014
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Alzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD. The data set used in this study was composed of EEG signals recorded from 2 groups: (S1) 74 normal subjects, 33 females and 41 males (mean age 67 years, standard deviation = 8) and (S2) 88 probable AD patients (NINCDS-ADRDA criteria), 55 females and 33 males (mean age 74.7 years, standard deviation = 7.8) with mild to moderate symptoms (DSM-IV-TR). Attention is given to sample size and the use of state of the art open source tools (LetsWave and WEKA) to process the EEG data. This innovative technique consists in associating Morlet wavelet filter with a support vector machine technique. A total of 111 EEG features (attributes) were obtained for 162 probands. The results were accuracy of 92.72% and area under the curve of 0.92 (percentage split test). Most important, comparing a single patient versus the total data set resulted in accuracy of 84.56% (leave-one-patient-out test). Particular emphasis was on clinical diagnosis and feasibility of implementation of this low-cost procedure, because programming knowledge is not required. Consequently, this new method can be useful to support AD diagnosis in resource-limited settings.
Palavras-chave
quantitative EEG (QEEG), Alzheimer's disease, wavelets, support vector machine (SVM)
URI
https://observatorio.fm.usp.br/handle/OPI/7539
Referências
  1. Ahmadlou M, 2010, CLIN EEG NEUROSCI, V41, P1
  2. Akin M, 2002, J Med Syst, V26, P241, DOI 10.1023/A:1015075101937
  3. Alzheimer's Association, 2012, ALZHEIMERS DEMENT, V8, P131, DOI 10.1016/J.JALZ.2012.02.001
  4. American Psychiatric Association, 2002, DIAGN STAT MAN MENT
  5. Anghinah R., 2003, ESTUDO DENSIDADE ESP
  6. [Anonymous], 2009, CLIN EEG NEUROSCI, V40, P43
  7. Arab MR, 2010, COMPUT BIOL MED, V40, P733, DOI 10.1016/j.compbiomed.2010.06.001
  8. Babiloni C, 2013, J ALZHEIMERS DIS, V34, P1015, DOI 10.3233/JAD-121750
  9. Babiloni C, 2010, J ALZHEIMERS DIS, V22, P1047, DOI 10.3233/JAD-2010-100798
  10. Bouckaert RR, 2010, J MACH LEARN RES, V11, P2533
  11. Brookmeyer R, 2007, ALZHEIMERS DEMENT, V3, P186, DOI 10.1016/j.jalz.2007.04.381
  12. Brucki SMD, 2003, ARQ NEURO-PSIQUIAT, V61, P777, DOI 10.1590/S0004-282X2003000500014
  13. Campbell C, 2001, STUD FUZZ SOFT COMP, V66, P155
  14. Chapelle O, 2002, MACH LEARN, V46, P131, DOI 10.1023/A:1012450327387
  15. Cristianini N., 2000, INTRO SUPPORT VECTOR
  16. Engel J, 2009, EPILEPSIA, V50, P598, DOI 10.1111/j.1528-1167.2008.01917.x
  17. Falk TH, 2012, EURASIP J ADV SIG PR, DOI 10.1186/1687-6180-2012-192
  18. Fan RE, 2008, J MACH LEARN RES, V9, P1871
  19. Fan RE, 2005, J MACH LEARN RES, V6, P1889
  20. FARGE M, 1992, ANNU REV FLUID MECH, V24, P395, DOI 10.1146/annurev.fluid.24.1.395
  21. Feng ZY, 2003, ACTA BIOCH BIOPH SIN, V35, P741
  22. Fonseca LC, 2009, CLIN EEG NEUROSCI, V40, P168
  23. Fonseca LC, 2011, CLIN EEG NEUROSCI, V42, P185
  24. Gao JF, 2012, CLIN EEG NEUROSCI, V43, P54, DOI 10.1177/1550059411428715
  25. GOUPILLAUD P, 1984, GEOEXPLORATION, V23, P85, DOI 10.1016/0016-7142(84)90025-5
  26. Herrera E, 2002, ALZ DIS ASSOC DIS, V16, P103, DOI 10.1097/01.WAD.0000020202.50697.df
  27. Herrera Jr E, 1988, REV PSIQ CLIN, V25, P70
  28. HIER DB, 1991, CLIN ELECTROENCEPHAL, V22, P178
  29. Hot P, 2011, BIOL PSYCHOL, V87, P334, DOI 10.1016/j.biopsycho.2011.04.002
  30. Iannetti GD, 2008, J NEUROPHYSIOL, V100, P815, DOI 10.1152/jn.00097.2008
  31. Keerthi SS, 2003, NEURAL COMPUT, V15, P1667, DOI 10.1162/089976603321891855
  32. Kempfner J, 2011, IEEE ENG MED BIO, P6063, DOI 10.1109/IEMBS.2011.6091498
  33. Khalighi S, 2011, IEEE ENG MED BIO, P3306, DOI 10.1109/IEMBS.2011.6090897
  34. Knott V, 1999, CLIN ELECTROENCEPHAL, V30, P64
  35. Lehmann C, 2007, J NEUROSCI METH, V161, P342, DOI 10.1016/j.jneumeth.2006.10.023
  36. LEHMANN D, 1971, ELECTROEN CLIN NEURO, V31, P439, DOI 10.1016/0013-4694(71)90165-9
  37. Lin W-C, 2004, THESIS U PITTSBURGH
  38. Lopes MA, 2002, ARQ NEURO-PSIQUIAT, V60, P61, DOI 10.1590/S0004-282X2002000100012
  39. MCKHANN G, 1984, NEUROLOGY, V34, P939
  40. MORRIS JC, 1993, NEUROLOGY, V43, P2412
  41. Mouraux A, LETSWAVE FREE EEG SI
  42. Muthuswamy J, 1998, J NEUROSCI METH, V83, P1, DOI 10.1016/S0165-0270(98)00065-X
  43. NIELSEN T, 1993, EUR NEUROL, V33, P173, DOI 10.1159/000116928
  44. Nitrini R, 2009, INT PSYCHOGERIATR, V21, P622, DOI 10.1017/S1041610209009430
  45. OTT A, 1995, BRIT MED J, V310, P970
  46. Petrosian AA, 2001, CLIN NEUROPHYSIOL, V112, P1378, DOI 10.1016/S1388-2457(01)00579-X
  47. Polikar R, 2007, COMPUT BIOL MED, V37, P542, DOI 10.1016/j.compbiomed.2006.08.012
  48. Rioul O, 1991, IEEE SIGNAL PROC MAG, V8, P14, DOI 10.1109/79.91217
  49. Sandmann MC, 1996, ARQ NEURO-PSIQUIAT, V54, P50
  50. Sankari Z, 2011, J NEUROSCI METH, V197, P165, DOI 10.1016/j.jneumeth.2011.01.027
  51. Sankari Z, 2012, CLIN EEG NEUROSCI, V43, P268, DOI 10.1177/1550059412444970
  52. Sarazin M, 2012, NEUROIMAG CLIN N AM, V22, P23, DOI 10.1016/j.nic.2011.11.004
  53. Schinkel S, 2007, COGN NEURODYNAMICS, V1, P317, DOI 10.1007/s11571-007-9023-z
  54. Selvam VS, 2011, IEEE ENG MED BIO, P6104, DOI 10.1109/IEMBS.2011.6091508
  55. Siuly, 2011, COMPUT METH PROG BIO, V104, P358, DOI 10.1016/j.cmpb.2010.11.014
  56. Sneddon R, 2006, CLIN EEG NEUROSCI, V37, P54
  57. Staudinger T, 2011, IEEE ENG MED BIO, P2033, DOI 10.1109/IEMBS.2011.6090374
  58. TERRY RD, 1994, PROG BRAIN RES, V101, P383
  59. Trambaiolli LR, 2011, IEEE ENG MED BIO, P3828, DOI 10.1109/IEMBS.2011.6090951
  60. Trambaiolli LR, 2011, CLIN EEG NEUROSCI, V42, P160
  61. Vapnik V. N., 1995, NATURE STAT LEARNING
  62. Vecchio F, 2011, INT J ALZHEIMERS DIS, V2011
  63. VERAS RP, 1991, REV SAUDE PUBL, V25, P476, DOI 10.1590/S0034-89101991000600010
  64. Wan BK, 2006, J NEURAL ENG, V3, P71, DOI 10.1088/1741-2560/3/1/008
  65. Wang XJ, 2010, PHYSIOL REV, V90, P1195, DOI 10.1152/physrev.00035.2008
  66. WEAVER JB, 1991, MAGNET RESON MED, V21, P288, DOI 10.1002/mrm.1910210213
  67. Witten IH, 2011, MOR KAUF D, P1
  68. Witten IH, 2005, DATA MINING PRACTICA
  69. Yuan GX, 2012, P IEEE, V100, P2584, DOI 10.1109/JPROC.2012.2188013
  70. Yuan Q, 2012, CLIN EEG NEUROSCI, V43, P127, DOI 10.1177/1550059411435861
  71. Zhao Jianlin, 2011, Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, V28, P277

Coleções
Artigos e Materiais de Revistas Científicas - FM/MNE
Artigos e Materiais de Revistas Científicas - HC/ICHC
Artigos e Materiais de Revistas Científicas - LIM/15