Dual tree complex wavelet transform based analysis of epileptiform discharges

Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 10; H. 4; S. 543 - 550
Hauptverfasser: Khan, Ayesha Tooba, Khan, Yusuf Uzzaman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.12.2018
Springer Nature B.V
Schlagworte:
Algorithms
Brain
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Convulsions & seizures
Discharge
Epilepsy
Feature extraction
Information Systems and Communication Service
Management of Computing and Information Systems
Original Research
Seizures
Software Engineering/Programming and Operating Systems
Wavelet analysis
Wavelet transforms
Dual tree complex wavelet transform
Seizure detection
EEG
K-fold cross validation
Classification
ISSN:2511-2104, 2511-2112
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is intended to provide insight to the epileptiform discharges during the seizures using dual tree complex wavelet transform. Algorithm is developed using publically available data from Bonn University. Statistical and nonlinear features, selected on the basis of Bhattacharyya distance, are extracted from EEG segments to demarcate the seizure and nonseizure EEG boundaries. Quadratic classification of EEG features followed by k-fold cross validation with varying train to test ratios is employed to develop a generalised robust model. Performance of classifier is accessed in terms of statistical parameters.
AbstractList Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is intended to provide insight to the epileptiform discharges during the seizures using dual tree complex wavelet transform. Algorithm is developed using publically available data from Bonn University. Statistical and nonlinear features, selected on the basis of Bhattacharyya distance, are extracted from EEG segments to demarcate the seizure and nonseizure EEG boundaries. Quadratic classification of EEG features followed by k-fold cross validation with varying train to test ratios is employed to develop a generalised robust model. Performance of classifier is accessed in terms of statistical parameters.
Author Khan, Ayesha Tooba
Khan, Yusuf Uzzaman
Author_xml – sequence: 1
  givenname: Ayesha Tooba
  surname: Khan
  fullname: Khan, Ayesha Tooba
  email: er.khanatk@gmail.com
  organization: Department of Electrical Engineering, Aligarh Muslim University
– sequence: 2
  givenname: Yusuf Uzzaman
  surname: Khan
  fullname: Khan, Yusuf Uzzaman
  organization: Department of Electrical Engineering, Aligarh Muslim University
BookMark eNp9kE1PwzAMQCM0JMbYD-BWiXMhTpomPaLxKQ1xgXOUpu4I6tqSdMD-PRlFICHBwbKV-DnOOySTtmuRkGOgp0CpPAsZKElTCipGVqRij0yZAEgZAJt81zQ7IPMQXEk5sJwLCVNyd7ExTTJ4xMR2677B9-TNvGKDQzw0bag7v05KE7BKTGuabXAh6eoEe9dgP7jP68oF-2T8CsMR2a9NE3D-lWfk8eryYXGTLu-vbxfny9QyDiJVklUQo1CW14xiiVKhkrkRNTc50kooUSlKS4qypFZkVhUlq5nkWQEgJJ-Rk3Fu77uXDYZBP3cbH_cLmsV_csXzjMUuGLus70LwWOveu7XxWw1U78TpUZyO4vROnBaRkb8Y6wYzuK6NOlzzL8lGMsRX2hX6n53-hj4ALxeDQg
CitedBy_id crossref_primary_10_1007_s41870_020_00596_5
Cites_doi 10.1109/ICIEV.2014.6850758
10.1109/MSP.2005.1550194
10.1016/j.eswa.2010.06.065
10.1155/2010/853434
10.1007/s11760-014-0736-2
10.1109/MCSE.2016.82
10.1016/j.eswa.2016.02.040
10.3390/s17030481
10.1109/TITB.2011.2181403
10.1016/j.bspc.2017.07.022
10.1103/PhysRevE.64.061907
10.3389/fninf.2017.00052
10.1016/S1388-2457(03)00035-X
10.1109/INDICON.2015.7443482
10.1016/j.compbiomed.2017.07.010
ContentType Journal Article
Copyright Bharati Vidyapeeth's Institute of Computer Applications and Management 2018
Copyright Springer Nature B.V. 2018
Copyright_xml – notice: Bharati Vidyapeeth's Institute of Computer Applications and Management 2018
– notice: Copyright Springer Nature B.V. 2018
DBID AAYXX
CITATION
7SC
8FD
JQ2
L7M
L~C
L~D
DOI 10.1007/s41870-018-0149-5
DatabaseName CrossRef
Computer and Information Systems Abstracts
Technology Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Computer and Information Systems Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Advanced Technologies Database with Aerospace
ProQuest Computer Science Collection
Computer and Information Systems Abstracts Professional
DatabaseTitleList Computer and Information Systems Abstracts
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Computer Science
EISSN 2511-2112
EndPage 550
ExternalDocumentID 10_1007_s41870_018_0149_5
GroupedDBID -EM
0R~
406
AACDK
AAHNG
AAIAL
AAJBT
AANZL
AASML
AATNV
AATVU
AAUYE
ABAKF
ABDZT
ABECU
ABFTV
ABJNI
ABJOX
ABKCH
ABMQK
ABQBU
ABTEG
ABTKH
ABTMW
ABXPI
ACAOD
ACDTI
ACGFS
ACHSB
ACMLO
ACOKC
ACPIV
ACZOJ
ADHHG
ADKNI
ADKPE
ADTPH
ADURQ
ADYFF
ADZKW
AEBTG
AEFQL
AEJRE
AEMSY
AEOHA
AESKC
AEVLU
AEXYK
AFBBN
AFQWF
AGDGC
AGMZJ
AGQEE
AGRTI
AHSBF
AIAKS
AIGIU
AILAN
AITGF
AJRNO
AJZVZ
ALFXC
ALMA_UNASSIGNED_HOLDINGS
AMKLP
AMXSW
AMYLF
AMYQR
AXYYD
BGNMA
CSCUP
DNIVK
DPUIP
EBLON
EBS
EIOEI
EJD
FERAY
FIGPU
FINBP
FNLPD
FSGXE
GGCAI
GJIRD
IKXTQ
IWAJR
J-C
JZLTJ
KOV
LLZTM
M4Y
NPVJJ
NQJWS
NU0
O9J
PT4
RLLFE
ROL
RSV
SISQX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
STPWE
TSG
UOJIU
UTJUX
UZXMN
VFIZW
Z7Z
Z81
Z83
Z88
ZMTXR
AAYXX
ABBRH
ABDBE
ABFSG
ABRTQ
ABUWG
ACSTC
AEZWR
AFDZB
AFHIU
AFKRA
AFOHR
AHPBZ
AHWEU
AIXLP
ARAPS
ATHPR
AYFIA
AZQEC
BENPR
BGLVJ
CCPQU
CITATION
DWQXO
GNUQQ
HCIFZ
K7-
PHGZM
PHGZT
PQGLB
7SC
8FD
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c2315-872d172d98c3f20ebe78e876a5f3a6e0d585d800b0e7b0c54c89b2f2734911573
IEDL.DBID RSV
ISSN 2511-2104
IngestDate Wed Sep 17 23:58:57 EDT 2025
Sat Nov 29 03:11:44 EST 2025
Tue Nov 18 21:43:05 EST 2025
Fri Feb 21 02:32:43 EST 2025
IsPeerReviewed false
IsScholarly true
Issue 4
Keywords Dual tree complex wavelet transform
Seizure detection
EEG
K-fold cross validation
Classification
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2315-872d172d98c3f20ebe78e876a5f3a6e0d585d800b0e7b0c54c89b2f2734911573
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
PQID 2210383642
PQPubID 2034493
PageCount 8
ParticipantIDs proquest_journals_2210383642
crossref_primary_10_1007_s41870_018_0149_5
crossref_citationtrail_10_1007_s41870_018_0149_5
springer_journals_10_1007_s41870_018_0149_5
PublicationCentury 2000
PublicationDate 20181200
PublicationDateYYYYMMDD 2018-12-01
PublicationDate_xml – month: 12
  year: 2018
  text: 20181200
PublicationDecade 2010
PublicationPlace Singapore
PublicationPlace_xml – name: Singapore
– name: Heidelberg
PublicationSubtitle An Official Journal of Bharati Vidyapeeth's Institute of Computer Applications and Management
PublicationTitle International journal of information technology (Singapore. Online)
PublicationTitleAbbrev Int. j. inf. tecnol
PublicationYear 2018
Publisher Springer Singapore
Springer Nature B.V
Publisher_xml – name: Springer Singapore
– name: Springer Nature B.V
References Farooq O, Khan YU (2010) Automatic seizure detection using higher order moments. In: International conference on recent trends in information, telecommunication and computing
WHO (2010) Epilepsy in the WHO Eastern Mediterranean Region: bridging the gap. Regional Office for the Eastern Mediterranean. http://apps.who.int/iris/handle/10665/119905. Accessed 12 Feb 2017
WHO (2017). Epilepsy fact sheet. http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 28 Feb 2017
SubasiAGursoyMIEEG signal classification using PCA, ICA, LDA and support vector machinesExpert Syst Appl201037128659866610.1016/j.eswa.2010.06.065
SelesnickIWBaraniukRGKingsburyNGThe dual tree complex wavelet transformIEEE Signal Process Mag20052212315110.1109/MSP.2005.1550194
Khan AT, Husain I, Khan YU (2015) Seizure onset patterns in EEG and their detection using statistical measures. In: 12th IEEE India international conference (INDICON) on electronics, energy, environment, communication, computer, control
BajajVPachoriRBClassification of seizure and nonseizure EEG signals using empirical mode decompositionIEEE Trans Inf Technol Biomed2012161135114110.1109/TITB.2011.2181403
LeeJParkJYangSKimHChoiYSKimHJLeeHWLeeBUEarly seizure detection by applying frequency-based algorithm derived from the principal component analysisFront Neuroinform2017111210.3389/fninf.2017.00052
DasABBhuiyanMIHAlamSSClassification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detectionSIViP201610225926610.1007/s11760-014-0736-2
ZahraAKanwalNUR RehmanNEhsanSMcDonald-MaierKDSeizure detection from EEG signals using multivariate empirical mode decompositionComput Biol Med20178813214110.1016/j.compbiomed.2017.07.010
KhanYUGotmanJWavelet based automatic seizure detection in intracerebral electroencephalogramClin Neurophysiol200311489890810.1016/S1388-2457(03)00035-X
LiangSFWangHCChangWLCombination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detectionEURASIP J Adv Signal Process20102010185343410.1155/2010/853434
SwamiPA novel robust diagnostic model to detect seizures in electroencephalographyExpert Syst Appl20165611613010.1016/j.eswa.2016.02.040
AndrzejakRGLehnertzKMormannFRiekeCDavidPElgerCEIndications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain statePhys Rev E20016406190710.1103/PhysRevE.64.061907
AlickovicEKevricJSubasiAPerformance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and predictionBiomed Signal Process Control2018399410210.1016/j.bspc.2017.07.022
Rafi N, Khan YU, Farooq O (2014) Epileptic seizure detection: reformation of the traditional method on scalp recorded electroencephalogram. In: International conference on emerging trends in electrical engineering
JooHSHanSHLeeJJangDPKangJKWooJSpectral analysis of acceleration data for detection of generalized tonic–clonic seizuresSensors20171748149210.3390/s17030481
Das AB, Bhuiyan MIH, Alam SS (2014) A statistical method for automatic detection of seizure and epilepsy in the dual tree complex wavelet transform domain. In: 3rd International conference on informatics, electronics & vision
SareenSSoodSKGuptaSKA cloud-based seizure alert system for epileptic patients that uses higher-order statisticsComput Sci Eng20165566710.1109/MCSE.2016.82
RG Andrzejak (149_CR14) 2001; 64
149_CR5
149_CR7
YU Khan (149_CR17) 2003; 114
149_CR18
149_CR1
149_CR2
IW Selesnick (149_CR16) 2005; 22
E Alickovic (149_CR3) 2018; 39
SF Liang (149_CR10) 2010; 2010
S Sareen (149_CR8) 2016; 5
J Lee (149_CR13) 2017; 11
A Zahra (149_CR4) 2017; 88
P Swami (149_CR19) 2016; 56
AB Das (149_CR6) 2016; 10
HS Joo (149_CR11) 2017; 17
A Subasi (149_CR12) 2010; 37
V Bajaj (149_CR15) 2012; 16
149_CR9
References_xml – reference: BajajVPachoriRBClassification of seizure and nonseizure EEG signals using empirical mode decompositionIEEE Trans Inf Technol Biomed2012161135114110.1109/TITB.2011.2181403
– reference: ZahraAKanwalNUR RehmanNEhsanSMcDonald-MaierKDSeizure detection from EEG signals using multivariate empirical mode decompositionComput Biol Med20178813214110.1016/j.compbiomed.2017.07.010
– reference: LiangSFWangHCChangWLCombination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detectionEURASIP J Adv Signal Process20102010185343410.1155/2010/853434
– reference: SubasiAGursoyMIEEG signal classification using PCA, ICA, LDA and support vector machinesExpert Syst Appl201037128659866610.1016/j.eswa.2010.06.065
– reference: KhanYUGotmanJWavelet based automatic seizure detection in intracerebral electroencephalogramClin Neurophysiol200311489890810.1016/S1388-2457(03)00035-X
– reference: Rafi N, Khan YU, Farooq O (2014) Epileptic seizure detection: reformation of the traditional method on scalp recorded electroencephalogram. In: International conference on emerging trends in electrical engineering
– reference: SareenSSoodSKGuptaSKA cloud-based seizure alert system for epileptic patients that uses higher-order statisticsComput Sci Eng20165566710.1109/MCSE.2016.82
– reference: SelesnickIWBaraniukRGKingsburyNGThe dual tree complex wavelet transformIEEE Signal Process Mag20052212315110.1109/MSP.2005.1550194
– reference: WHO (2010) Epilepsy in the WHO Eastern Mediterranean Region: bridging the gap. Regional Office for the Eastern Mediterranean. http://apps.who.int/iris/handle/10665/119905. Accessed 12 Feb 2017
– reference: DasABBhuiyanMIHAlamSSClassification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detectionSIViP201610225926610.1007/s11760-014-0736-2
– reference: JooHSHanSHLeeJJangDPKangJKWooJSpectral analysis of acceleration data for detection of generalized tonic–clonic seizuresSensors20171748149210.3390/s17030481
– reference: Farooq O, Khan YU (2010) Automatic seizure detection using higher order moments. In: International conference on recent trends in information, telecommunication and computing
– reference: AlickovicEKevricJSubasiAPerformance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and predictionBiomed Signal Process Control2018399410210.1016/j.bspc.2017.07.022
– reference: SwamiPA novel robust diagnostic model to detect seizures in electroencephalographyExpert Syst Appl20165611613010.1016/j.eswa.2016.02.040
– reference: Das AB, Bhuiyan MIH, Alam SS (2014) A statistical method for automatic detection of seizure and epilepsy in the dual tree complex wavelet transform domain. In: 3rd International conference on informatics, electronics & vision
– reference: AndrzejakRGLehnertzKMormannFRiekeCDavidPElgerCEIndications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain statePhys Rev E20016406190710.1103/PhysRevE.64.061907
– reference: WHO (2017). Epilepsy fact sheet. http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 28 Feb 2017
– reference: Khan AT, Husain I, Khan YU (2015) Seizure onset patterns in EEG and their detection using statistical measures. In: 12th IEEE India international conference (INDICON) on electronics, energy, environment, communication, computer, control
– reference: LeeJParkJYangSKimHChoiYSKimHJLeeHWLeeBUEarly seizure detection by applying frequency-based algorithm derived from the principal component analysisFront Neuroinform2017111210.3389/fninf.2017.00052
– ident: 149_CR1
– ident: 149_CR7
  doi: 10.1109/ICIEV.2014.6850758
– volume: 22
  start-page: 123
  year: 2005
  ident: 149_CR16
  publication-title: IEEE Signal Process Mag
  doi: 10.1109/MSP.2005.1550194
– volume: 37
  start-page: 8659
  issue: 12
  year: 2010
  ident: 149_CR12
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2010.06.065
– ident: 149_CR2
– ident: 149_CR5
– volume: 2010
  start-page: 853434
  issue: 1
  year: 2010
  ident: 149_CR10
  publication-title: EURASIP J Adv Signal Process
  doi: 10.1155/2010/853434
– volume: 10
  start-page: 259
  issue: 2
  year: 2016
  ident: 149_CR6
  publication-title: SIViP
  doi: 10.1007/s11760-014-0736-2
– volume: 5
  start-page: 56
  year: 2016
  ident: 149_CR8
  publication-title: Comput Sci Eng
  doi: 10.1109/MCSE.2016.82
– volume: 56
  start-page: 116
  year: 2016
  ident: 149_CR19
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2016.02.040
– volume: 17
  start-page: 481
  year: 2017
  ident: 149_CR11
  publication-title: Sensors
  doi: 10.3390/s17030481
– volume: 16
  start-page: 1135
  year: 2012
  ident: 149_CR15
  publication-title: IEEE Trans Inf Technol Biomed
  doi: 10.1109/TITB.2011.2181403
– volume: 39
  start-page: 94
  year: 2018
  ident: 149_CR3
  publication-title: Biomed Signal Process Control
  doi: 10.1016/j.bspc.2017.07.022
– volume: 64
  start-page: 061907
  year: 2001
  ident: 149_CR14
  publication-title: Phys Rev E
  doi: 10.1103/PhysRevE.64.061907
– ident: 149_CR9
– volume: 11
  start-page: 1
  year: 2017
  ident: 149_CR13
  publication-title: Front Neuroinform
  doi: 10.3389/fninf.2017.00052
– volume: 114
  start-page: 898
  year: 2003
  ident: 149_CR17
  publication-title: Clin Neurophysiol
  doi: 10.1016/S1388-2457(03)00035-X
– ident: 149_CR18
  doi: 10.1109/INDICON.2015.7443482
– volume: 88
  start-page: 132
  year: 2017
  ident: 149_CR4
  publication-title: Comput Biol Med
  doi: 10.1016/j.compbiomed.2017.07.010
SSID ssib031263571
ssj0002710285
Score 2.0634208
Snippet Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing...
SourceID proquest
crossref
springer
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 543
SubjectTerms Algorithms
Brain
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Convulsions & seizures
Discharge
Epilepsy
Feature extraction
Information Systems and Communication Service
Management of Computing and Information Systems
Original Research
Seizures
Software Engineering/Programming and Operating Systems
Wavelet analysis
Wavelet transforms
Title Dual tree complex wavelet transform based analysis of epileptiform discharges
URI https://link.springer.com/article/10.1007/s41870-018-0149-5
https://www.proquest.com/docview/2210383642
Volume 10
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAVX
  databaseName: SpringerLINK Contemporary 1997-Present
  customDbUrl:
  eissn: 2511-2112
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002710285
  issn: 2511-2104
  databaseCode: RSV
  dateStart: 20170301
  isFulltext: true
  titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22
  providerName: Springer Nature
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZgcIADgwFiMFAOnECVkjZd2yPiIS5MiJd2q7oklUBom9YN-PnYWbIBAiS4Ng9VdmJ_lp3PAIfo9WQmjAjiRGCAIrkKirbhgdQ8UangWprCNptIOp20282u3Tvuyle7-5SktdSzx25SpNQkRVDxlcyCeBGW0Nul1K_h5vbBH6JIEL2KwzhPNrNGPpRKGQlNBxjiSJ_d_G7Xz_5pDjq_5Emt-7mo_-vH12HNoU12Mj0eG7Bg-g2o-04OzF3sBqx-oCXchKuzCS6idDWzFefmjb0W1KBijB8dzmXk_jQrHKcJG5TMDNHCoAWyw_Tal0iYTLUF9xfnd6eXgWu7ECgEezHax1AjrNFZqqIy5KjlJDVoNIu4jEiPGiMMjTizx03S4yqWKs16YUk8ORlR90TbUOsP-maH6qZ0Ysp2hBizpMixh-hMGgQdYaGyWIomcC_sXDlOcmqN8ZzP2JSt8HIUXk7Cy-MmHM2WDKeEHL9NbnkN5u5uVnkYEil8hIFXE469xubDP262-6fZe7ASkspt5UsLauPRxOzDsnoZP1ajA3tk3wFQm-Cm
linkProvider Springer Nature
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEB60CurBalWsVs3BkxLIY9MkR1FLxbaIVuktJLsbUKQtTav-fGe2u62KCnrNPggzuzPfMLPfAByj12OxK107CF0MUJjD7bQuHZsJJ-SR6wgmU9VsIux0ol4vvtHvuAtT7W5SkspSzx67MTeiJikuFV-x2A4WYYmhwyLC_Nu7B3OIfJfoVTTGeVKZNfKhVMpIaNrGEIeZ7OZ3u372T3PQ-SVPqtxPo_yvH9-AdY02rbPp8diEBdmvQNl0crD0xa7A2gdawi1oX0xwEaWrLVVxLt-s15QaVIzxo8a5Frk_YaWa08Qa5JYcooVBC6SG6bUvkTDJYhvuG5fd86at2y7YHMFegPbREwhrRBxxP_cc1HIYSTSaaZD7pEeBEYZAnJk5MswcHjAexZmXE09OTNQ9_g6U-oO-3KW6KRHKvO4jxswpcswQnTGJoMNLeRwwtwqOEXbCNSc5tcZ4TmZsykp4CQovIeElQRVOZkuGU0KO3ybXjAYTfTeLxPOIFN7HwKsKp0Zj8-EfN9v70-wjWGl2262kddW53odVj9SvqmBqUBqPJvIAlvnL-LEYHarj-w7giOOK
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB60iujBalWsVt2DJyWYx6ZJjmItiloKPuhtSXc3oEhamlT9-c6km1ZFBfGafUBmdme-YWa_AThEr8cjRzuWHzgYoHBbWnFT2xZXdiBDx1Zcx0WziaDTCXu9qGv6nGZltXuZkpy8aSCWpjQ_GarkZPrwjTshNUxxqBCLR5Y_Dwuc6ugpXL99KA-U5xDVisE7T0WWjfwplTUSsrYw3OFlpvO7XT_7qhkA_ZIzLVxRu_rvn1iDVYNC2enk2KzDnE5rUC07PDBz4Wuw8oGucANuWmNcRGlsVlSi6zf2GlPjihw_GvzLyC0qFhuuEzZImB6i5UHLVAzTK2AiZ9LZJty3z-_OLizTjsGSCAJ9tJuuQrijolB6iWuj9oNQozGN_cQj_SqMPBTiz76tg74tfS7DqO8mxJ8TEaWPtwWVdJDqbaqnUoFOmh5iz4Qiyj6iNq4RjLixjHzu1MEuBS-k4SqnlhnPYsqyXAhPoPAECU_4dTiaLhlOiDp-m9wotSnMnc2E6xJZvIcBWR2OS-3Nhn_cbOdPsw9gqdtqi-vLztUuLLuk_aI4pgGVfDTWe7AoX_LHbLRfnOR36q7sbg
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=Dual+tree+complex+wavelet+transform+based+analysis+of+epileptiform+discharges&rft.jtitle=International+journal+of+information+technology+%28Singapore.+Online%29&rft.au=Khan%2C+Ayesha+Tooba&rft.au=Khan%2C+Yusuf+Uzzaman&rft.date=2018-12-01&rft.issn=2511-2104&rft.eissn=2511-2112&rft.volume=10&rft.issue=4&rft.spage=543&rft.epage=550&rft_id=info:doi/10.1007%2Fs41870-018-0149-5&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s41870_018_0149_5
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2511-2104&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2511-2104&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2511-2104&client=summon