Application of Paraconsistent Artificial Neural Networks as a Method of Aid in the Diagnosis of Alzheimer Disease
The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician int...
Saved in:
Published in | Journal of medical systems Vol. 34; no. 6; pp. 1073 - 1081 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Boston
Springer US
01.12.2010
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 0148-5598 1573-689X |
DOI | 10.1007/s10916-009-9325-2 |
Cover
Abstract | The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis. |
---|---|
AbstractList | The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis. The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis.The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis. The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis.[PUBLICATION ABSTRACT] The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0Hz (with an average frequency of 10Hz), allowing a range of 0.5Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis. |
Author | da Silva Lopes, Helder Frederico Abe, Jair M. Anghinah, Renato |
Author_xml | – sequence: 1 givenname: Helder Frederico surname: da Silva Lopes fullname: da Silva Lopes, Helder Frederico email: helder@autobyte.com.br organization: Medicine School, University of São Paulo – sequence: 2 givenname: Jair M. surname: Abe fullname: Abe, Jair M. organization: Institute for Advanced Studies, University of São Paulo – sequence: 3 givenname: Renato surname: Anghinah fullname: Anghinah, Renato organization: Reference Center of Behavioral Disturbances and Dementia (CEREDIC) of Medicine School, University of São Paulo |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20703601$$D View this record in MEDLINE/PubMed |
BookMark | eNqFkU1rVDEUhoNU7LT6A9xIcOPqapJ787UcWqtC_VgouAuZ3JNO6p1kmuQi9tebmWkRCiokHDg8z-Ek7wk6iikCQs8peU0JkW8KJZqKjhDd6Z7xjj1CC8pl3wmlvx-hBaGD6jjX6hidlHJNGiiEfIKOGZGkF4Qu0M1yu52CszWkiJPHX2y2LsUSSoVY8TLX4IMLdsKfYM77Un-m_KNg2w7-CHWdxp24DCMOEdc14PNgr2JqI_b96XYNYQO5tQvYAk_RY2-nAs_u6in6dvH269n77vLzuw9ny8vODYTVbuVGL1agWT9K5bn1bHQeGPfacz1KzVi73vvBWSd6PlDH3SC0Igw4EA39KXp1mLvN6WaGUs0mFAfTZCOkuRglKB-YUPS_pOSas4H0QyNfPiCv05xje4ZRRApGlJYNenEHzasNjGabw8bmX-b-0xsgD4DLqZQM3rhQ9wnUbMNkKDG7eM0hXtNSM7t4DWsmfWDeD_-Xww5OaWy8gvxn579LvwGYnLam |
CitedBy_id | crossref_primary_10_1007_s10916_015_0369_1 crossref_primary_10_1016_j_clinbiochem_2019_07_008 crossref_primary_10_1016_j_ijepes_2021_107317 crossref_primary_10_1145_3344998 crossref_primary_10_3233_ADR_200263 crossref_primary_10_1016_j_bbr_2017_03_012 crossref_primary_10_52547_shefa_9_1_152 crossref_primary_10_1007_s10916_015_0396_y crossref_primary_10_1016_j_ins_2017_08_074 crossref_primary_10_4018_IJSSCI_312553 crossref_primary_10_4103_jmss_JMSS_11_20 crossref_primary_10_1155_2018_5174815 crossref_primary_10_1016_j_procs_2013_09_194 crossref_primary_10_4155_fsoa_2017_0138 |
Cites_doi | 10.1016/S0140-6736(95)91804-3 10.1007/BF02345969 10.1002/ana.410160403 10.1016/j.cmpb.2005.02.006 10.1126/science.1411538 10.1007/s10916-007-9102-z 10.1016/j.neunet.2005.01.006 10.1016/S0013-4694(97)00106-5 10.1590/S0004-282X2005000400033 |
ContentType | Journal Article |
Copyright | Springer Science+Business Media, LLC 2009 Springer Science+Business Media, LLC 2010 |
Copyright_xml | – notice: Springer Science+Business Media, LLC 2009 – notice: Springer Science+Business Media, LLC 2010 |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QF 7QO 7QQ 7RV 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7X7 7XB 88C 88E 88I 8AL 8AO 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K7- K9. KB0 KR7 L7M LK8 L~C L~D M0N M0S M0T M1P M2P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 |
DOI | 10.1007/s10916-009-9325-2 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Nursing & Allied Health Database Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection (via ProQuest SciTech Premium Collection) Natural Science Collection ProQuest One Community College ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Healthcare Administration Database Medical Database Science Database Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Aluminium Industry Abstracts ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) ProQuest Nursing & Allied Health Source (Alumni) Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest Computing ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest Health Management ProQuest Nursing & Allied Health Source ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | MEDLINE MEDLINE - Academic Materials Research Database Engineering Research Database |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Public Health |
EISSN | 1573-689X |
EndPage | 1081 |
ExternalDocumentID | 2191651301 20703601 10_1007_s10916_009_9325_2 |
Genre | Journal Article Comparative Study |
GroupedDBID | --- -53 -5D -5G -BR -EM -Y2 -~C .86 .GJ .VR 04C 06C 06D 0R~ 0VY 199 1SB 2.D 203 28- 29L 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3SX 3V. 4.4 406 408 409 40E 53G 5GY 5QI 5RE 5VS 67Z 6NX 77K 78A 7RV 7X7 88E 88I 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACDTI ACGFO ACGFS ACGOD ACHSB ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG AQUVI ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BAPOH BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKEYQ BMSDO BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBD EBLON EBS EIHBH EIOEI EJD EMB EMOBN EN4 EPAXT ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW KPH LAK LK8 LLZTM M0N M0T M1P M2P M4Y M7P MA- MK0 N2Q NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZD RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SV3 SZ9 SZN T13 T16 TEORI TN5 TSG TSK TSV TT1 TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WJK WK8 WOW YLTOR Z45 Z7R Z7U Z7X Z7Z Z81 Z82 Z83 Z87 Z88 Z8M Z8R Z8T Z8W Z92 ZMTXR ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB PUEGO CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7XB 8AL 8BQ 8FD 8FK F28 FR3 H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 |
ID | FETCH-LOGICAL-c402t-bcdf6be923d78f5af2dcfe25f9f59d7922792fff4cac63541c5c469802e5e09e3 |
IEDL.DBID | BENPR |
ISSN | 0148-5598 |
IngestDate | Fri Sep 05 06:23:21 EDT 2025 Fri Sep 05 08:29:49 EDT 2025 Fri Jul 25 19:30:59 EDT 2025 Mon Jul 21 06:01:47 EDT 2025 Wed Oct 01 04:08:29 EDT 2025 Thu Apr 24 23:12:50 EDT 2025 Fri Feb 21 02:25:34 EST 2025 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6 |
Keywords | Pattern recognition Artificial neural network Electroencephalogram Alzheimer disease Paraconsistent logic |
Language | English |
License | http://www.springer.com/tdm |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c402t-bcdf6be923d78f5af2dcfe25f9f59d7922792fff4cac63541c5c469802e5e09e3 |
Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
PMID | 20703601 |
PQID | 807620897 |
PQPubID | 54050 |
PageCount | 9 |
ParticipantIDs | proquest_miscellaneous_861542681 proquest_miscellaneous_759524034 proquest_journals_807620897 pubmed_primary_20703601 crossref_citationtrail_10_1007_s10916_009_9325_2 crossref_primary_10_1007_s10916_009_9325_2 springer_journals_10_1007_s10916_009_9325_2 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 20101200 2010-12-00 2010-Dec 20101201 |
PublicationDateYYYYMMDD | 2010-12-01 |
PublicationDate_xml | – month: 12 year: 2010 text: 20101200 |
PublicationDecade | 2010 |
PublicationPlace | Boston |
PublicationPlace_xml | – name: Boston – name: United States – name: New York |
PublicationTitle | Journal of medical systems |
PublicationTitleAbbrev | J Med Syst |
PublicationTitleAlternate | J Med Syst |
PublicationYear | 2010 |
Publisher | Springer US Springer Nature B.V |
Publisher_xml | – name: Springer US – name: Springer Nature B.V |
References | Fausett (CR10) 1994 CR4 CR6 CR5 CR8 Kocyigit, Alkan, Erol (CR16) 2008; 32 CR7 CR17 CR13 Nitrini, Caramelli, Bottino, Damasceno, Brucki, Anghinah (CR3) 2005; 63 Erganian, Mahmoudi (CR9) 2005; 43 Weinstein, Kohn, Grever (CR14) 1992; 258 Nuwer, Comi, Emerson, Fuglsang-Frederiksen, Guérit, Hinrichs, Ikeda, Luccas, Rappelsberger (CR2) 1998; 106 Ventouras, Monoyou, Ktonas, Paparrigopoulos, Dikeos, Uzunoglu, Soldatos (CR12) 2005; 78 Subasi, Alkan, Koklukaya, Kiymik (CR11) 2005; 18 Duffy, Albert, Mcnulty, Garvey (CR1) 1984; 16 Baxt (CR15) 1995; 346 R. Nitrini (9325_CR3) 2005; 63 J. Weinstein (9325_CR14) 1992; 258 9325_CR13 9325_CR4 9325_CR7 A. Erganian (9325_CR9) 2005; 43 9325_CR17 9325_CR8 9325_CR5 9325_CR6 A. Subasi (9325_CR11) 2005; 18 F. H. Duffy (9325_CR1) 1984; 16 E. M. Ventouras (9325_CR12) 2005; 78 W. J. Baxt (9325_CR15) 1995; 346 Y. Kocyigit (9325_CR16) 2008; 32 L. Fausett (9325_CR10) 1994 M. R. Nuwer (9325_CR2) 1998; 106 16172732 - Arq Neuropsiquiatr. 2005 Sep;63(3A):713-9 7475607 - Lancet. 1995 Oct 28;346(8983):1135-8 1411538 - Science. 1992 Oct 16;258(5081):447-51 15899305 - Comput Methods Programs Biomed. 2005 Jun;78(3):191-207 6497352 - Ann Neurol. 1984 Oct;16(4):430-8 9743285 - Electroencephalogr Clin Neurophysiol. 1998 Mar;106(3):259-61 18333401 - J Med Syst. 2008 Feb;32(1):17-20 15921885 - Neural Netw. 2005 Sep;18(7):985-97 15865142 - Med Biol Eng Comput. 2005 Mar;43(2):296-305 |
References_xml | – volume: 346 start-page: 1135 year: 1995 end-page: 1138 ident: CR15 article-title: Application of artificial neural network to clinical medicine publication-title: Lancet doi: 10.1016/S0140-6736(95)91804-3 – year: 1994 ident: CR10 publication-title: Fundamentals of neural network architectures, algorithms, and applications – ident: CR4 – volume: 43 start-page: 296 issue: 2 year: 2005 end-page: 305 ident: CR9 article-title: Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain–computer interface. publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02345969 – ident: CR17 – ident: CR13 – volume: 16 start-page: 430 year: 1984 end-page: 438 ident: CR1 article-title: Age differences in brain electrical activity of healthy subjects publication-title: Ann Neural doi: 10.1002/ana.410160403 – volume: 63 start-page: 713 issue: 3A year: 2005 end-page: 719 ident: CR3 article-title: Diagnosis of Alzheimer's disease in Brazil: diagnostic criteria and auxiliary tests. Recommendations of the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology publication-title: Arq Neuropsiquiatr – volume: 78 start-page: 191 issue: 3 year: 2005 end-page: 207 ident: CR12 article-title: Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study publication-title: Comput Methods Programs Biomed. doi: 10.1016/j.cmpb.2005.02.006 – volume: 258 start-page: 447 year: 1992 end-page: 451 ident: CR14 article-title: Neural computing in cancer drug development: predicting mechanism of action publication-title: Science doi: 10.1126/science.1411538 – ident: CR6 – ident: CR5 – volume: 32 start-page: 17 issue: 1 year: 2008 end-page: 20 ident: CR16 article-title: Classification of EEG recordings by using fast independent component analysis and artificial neural network publication-title: J. Med. Syst. doi: 10.1007/s10916-007-9102-z – ident: CR7 – ident: CR8 – volume: 18 start-page: 985 issue: 7 year: 2005 end-page: 997 ident: CR11 article-title: Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing publication-title: Neural Networks doi: 10.1016/j.neunet.2005.01.006 – volume: 106 start-page: 259 year: 1998 end-page: 261 ident: CR2 article-title: IFCN standards for digital recording of clinical EEG publication-title: Electroencephalogr. Clin. Neurophysiol doi: 10.1016/S0013-4694(97)00106-5 – volume: 63 start-page: 713 issue: 3A year: 2005 ident: 9325_CR3 publication-title: Arq Neuropsiquiatr doi: 10.1590/S0004-282X2005000400033 – volume: 32 start-page: 17 issue: 1 year: 2008 ident: 9325_CR16 publication-title: J. Med. Syst. doi: 10.1007/s10916-007-9102-z – volume: 78 start-page: 191 issue: 3 year: 2005 ident: 9325_CR12 publication-title: Comput Methods Programs Biomed. doi: 10.1016/j.cmpb.2005.02.006 – volume: 258 start-page: 447 year: 1992 ident: 9325_CR14 publication-title: Science doi: 10.1126/science.1411538 – ident: 9325_CR13 – ident: 9325_CR17 – ident: 9325_CR7 – ident: 9325_CR8 – volume: 18 start-page: 985 issue: 7 year: 2005 ident: 9325_CR11 publication-title: Neural Networks doi: 10.1016/j.neunet.2005.01.006 – volume: 346 start-page: 1135 year: 1995 ident: 9325_CR15 publication-title: Lancet doi: 10.1016/S0140-6736(95)91804-3 – volume: 16 start-page: 430 year: 1984 ident: 9325_CR1 publication-title: Ann Neural doi: 10.1002/ana.410160403 – volume: 43 start-page: 296 issue: 2 year: 2005 ident: 9325_CR9 publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02345969 – volume: 106 start-page: 259 year: 1998 ident: 9325_CR2 publication-title: Electroencephalogr. Clin. Neurophysiol doi: 10.1016/S0013-4694(97)00106-5 – ident: 9325_CR5 – ident: 9325_CR6 – ident: 9325_CR4 – volume-title: Fundamentals of neural network architectures, algorithms, and applications year: 1994 ident: 9325_CR10 – reference: 15921885 - Neural Netw. 2005 Sep;18(7):985-97 – reference: 15865142 - Med Biol Eng Comput. 2005 Mar;43(2):296-305 – reference: 1411538 - Science. 1992 Oct 16;258(5081):447-51 – reference: 6497352 - Ann Neurol. 1984 Oct;16(4):430-8 – reference: 7475607 - Lancet. 1995 Oct 28;346(8983):1135-8 – reference: 9743285 - Electroencephalogr Clin Neurophysiol. 1998 Mar;106(3):259-61 – reference: 15899305 - Comput Methods Programs Biomed. 2005 Jun;78(3):191-207 – reference: 18333401 - J Med Syst. 2008 Feb;32(1):17-20 – reference: 16172732 - Arq Neuropsiquiatr. 2005 Sep;63(3A):713-9 |
SSID | ssj0009667 |
Score | 2.0035884 |
Snippet | The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some... |
SourceID | proquest pubmed crossref springer |
SourceType | Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 1073 |
SubjectTerms | Alzheimer Disease - diagnosis Alzheimer's disease Electroencephalography Health Informatics Health Sciences Humans Image Processing, Computer-Assisted Medical diagnosis Medical technology Medicine Medicine & Public Health Neural networks Neural Networks (Computer) Original Paper Statistics for Life Sciences |
SummonAdditionalLinks | – databaseName: SpringerLINK - Czech Republic Consortium dbid: AGYKE link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB7KLpRASNs83bRFh5xaFBytZFvHpU0aGjbkkEB6MtaLLEm8bey97K_vSLJ326YpBAwGWZJleaT5pJn5BHDgTT-pKRR1OteUK11RyR0OPJUjfFCC5cZv6E_Os9Mr_u1aXHdx3E3v7d6bJMNM_VuwG0IZ6jfzEXMIivPuUCA6FgMYjr9-Pztece1mWYyS5gX1_OO9MfNflfypjh5hzEf20aB2Tl7BZd_g6G1yezhv1aFe_MXl-Mwveg0bHQwl4yg3b-CFrTfh5aQztG_CetzOIzFKaQt-jleGbjJz5MLTPHvfWhSSug31RCoK4tk-wi24lzekwotMwjnVvuB4asi0Jgg7yZfo5TdtQvrd4sZO7-0DJgeT0TZcnRxffj6l3WkNVOMatKVKG5cpi4DR5IUTlWNGO8uEk05Ik8tAVeic47rSiHL4kRbaH1-ZMitsKu1oBwb1rLZ7QHDNyaoR5zKtpA_EKxQzo0yhllXcOWUSSPufVuqOytyfqHFXrkiYfdeW2LWl79qSJfBxWeRH5PH4X-b9XhLKbkg3ZZGi3kgLmSdAlk9xLHoDS1Xb2bwpcyGF5zfkT2cpEEEiKCqOEtiNIrZsDvOzL66PE_jUi8vq7U-29e2zcu_DGlv647yDQfswt-8RVbXqQzeKfgHbRBZm priority: 102 providerName: Springer Nature |
Title | Application of Paraconsistent Artificial Neural Networks as a Method of Aid in the Diagnosis of Alzheimer Disease |
URI | https://link.springer.com/article/10.1007/s10916-009-9325-2 https://www.ncbi.nlm.nih.gov/pubmed/20703601 https://www.proquest.com/docview/807620897 https://www.proquest.com/docview/759524034 https://www.proquest.com/docview/861542681 |
Volume | 34 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
journalDatabaseRights | – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1573-689X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: AFBBN dateStart: 19970201 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1573-689X dateEnd: 20171231 omitProxy: true ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: 7X7 dateStart: 19970201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1573-689X dateEnd: 20171231 omitProxy: true ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: BENPR dateStart: 19970201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1573-689X dateEnd: 20241005 omitProxy: true ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: 8FG dateStart: 19970201 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1573-689X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1573-689X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1ba9swFD60CYzBGF1389oFPexpQ8xVJF8exnC3pGUjpYwFsidj3Wigc9omfdmv3znyJZTSgrFBvgkfSf6k75zvAHwg6ie2mebepIZLbSqeS48dT6cIH7QSqaUF_dlZcjqXPxZqsQOzLhaG3Cq7MTEM1HZlaI38c4YTbhFnefr16ppT0igiV7sMGlWbWcF-CQpjuzCkEVkOYHg8OTv_tVXhTZImflpmnJTJO5qziaVDpMSJK0BIo7i4-6O6hz7vMafhhzTdg-ctkmRFY_oXsOPqfXgya7nyfXjWrMixJtDoJVwXW66arTw7J6Vmco9FO9eb8JxGTYKRYEc4BA_xNatwY7OQappuLJaWLWuGyJF9bxz1lutQfvnvwi3_uhssDqzPK5hPJ7-_nfI24QI3OI3ccG2sT7RDzGfTzKvKC2u8E8rnXuU2zYPaoPdemsogUJFHRhnKQBkLp1ycu_FrGNSr2r0FhtNGUY2lzOMqp1i6TAs7TjSaRUvvtY0g7r5uaVo1ckqKcVludZTJICUapCSDlCKCj_0tV40Ux2MXH3QmK9teuS77NhQB689idyKOpKrd6nZdpipXJFEoH74kQxCIuCY7iuBN0xb66ggaQHGKG8GnrnFs3_5gXd89WtcDeCp6F5pDGGxubt17BEIbPYLddJHiPpuejGBYnPz5ORm1TR5L56L4D5taB44 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ZaxRBEC7iBlQQ0XiN8egHfVEaJ73dczwEWU3CxmSXIAnkbTJ94UKcTTIbRP-b_82qnmORkLwFBgbmbLqqu7_qqvoK4B25fmKbae5NarjUpuS59DjwdIrwQSuRWtrQn0yT8ZH8dqyOV-BvlwtDYZXdnBgmajs3tEf-KUODW8RZnn4-O-dUNIqcq10FjbKtrGA3A8NYm9ex537_Qguu3tzdQnG_F2Jn-_DrmLdFBrhB02nBtbE-0Q5xjk0zr0ovrPFOKJ97lds0Dwx73ntpSoOLs9wwylDVxVg45eLcDfG7d2AVUYeQA1j9sj09-L5k_U2SJl9bZpyY0Du3apO7h8iMk28CIZTi4v-F8QraveKpDQvgziN42CJXNmpU7TGsuGoN7k5a3_waPGh2AFmT2PQEzkdL3zibe3ZAzNAUjot6VS3Cdxr2CkYEIeEUItJrVuLBJqG0Nb04mlk2qxgiVbbVBAbO6nD99M8PN_vpLvBy8DI9haNb6ftnMKjmlXsBDM1UUQ6lzOMyp9y9TAs7TDQuzFp6r20Ecde7hWnZz6kIx2mx5G0mgRQokIIEUogIPvSvnDXUHzc9vN6JrGhngbrodTYC1t_F4Us-mbJy88u6SFWuiBJRXv9IhqATcVS2EcHzRhf65giasNGkjuBjpxzLv1_b1pc3tvUt3BsfTvaL_d3p3jrcF334zisYLC4u3WsEYQv9plV1Bie3Pbr-Ad_JQbU |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9RAEB9qhSKIaP2K9WMf9EVZmu7t5uNB5PA8W-uVPljoW8x-4UHNtc0V0f_M_86Z3SSHlPatEAjkktySmdn9zc7MbwBeU-gntYXm3uSGS21qXkqPhqdzhA9aidzShv7sINs9kl-O1fEa_O1rYSitsp8Tw0RtF4b2yLcLdLhFWpT5tu-yIg4n0w-nZ5waSFGgte-mETVk3_3-hd5b-35vgqJ-I8T007ePu7xrMMANuk1Lro31mXaIcWxeeFV7YY13QvnSq9LmZWDX895LUxtcmOWOUYY6LqbCKZeWboTvvQW38xF6elSkPv284vvNslipLQtOHOh9QDVW7SEm4xSVQPCkuPh_SbyEcy_FaMPSN70P9zrMysZRyR7Amms2YWPWReU34W7c-2OxpOkhnI1XUXG28OyQOKEpERc1qlmG90TeCkbUIOEUctFbVuPBZqGpNT04nls2bxhiVDaJKYHzNlw_-fPDzX-6c7wc4kuP4OhGvvxjWG8WjXsKDB1UUY-kLNO6pKq9Qgs7yjQuyVp6r20Caf91K9PxnlP7jZNqxdhMAqlQIBUJpBIJvB0eOY2kH9fdvNWLrOrsv60GbU2ADb-i4VI0pm7c4qKtclUqIkOUV99SINxEBFXsJPAk6sIwHEFTNTrTCbzrlWP171eO9dm1Y30FG2hT1de9g_0tuCOGvJ3nsL48v3AvEH0t9cug5wy-37Rh_QMNHD9Q |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Application+of+paraconsistent+artificial+neural+networks+as+a+method+of+aid+in+the+diagnosis+of+Alzheimer+disease&rft.jtitle=Journal+of+medical+systems&rft.au=da+Silva+Lopes%2C+Helder+Frederico&rft.au=Abe%2C+Jair+M&rft.au=Anghinah%2C+Renato&rft.date=2010-12-01&rft.issn=0148-5598&rft.volume=34&rft.issue=6&rft.spage=1073&rft_id=info:doi/10.1007%2Fs10916-009-9325-2&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0148-5598&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0148-5598&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0148-5598&client=summon |