Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm
•A new method for discrimination of seven types of ECG beats is proposed.•This method employs a combination of morphology, frequency, and nonlinear indices.•Application of feature selection (include GA, PSO, DE, and NSGA-II) is discussed.•KNN, RBFNN, FF-net, Fit-net, and Pat-net are employed for cla...
Gespeichert in:
| Veröffentlicht in: | Expert systems with applications Jg. 161; S. 113697 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Elsevier Ltd
15.12.2020
Elsevier BV |
| Schlagworte: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A new method for discrimination of seven types of ECG beats is proposed.•This method employs a combination of morphology, frequency, and nonlinear indices.•Application of feature selection (include GA, PSO, DE, and NSGA-II) is discussed.•KNN, RBFNN, FF-net, Fit-net, and Pat-net are employed for classification.•High classification accuracy 98.75% is obtained based on the proposed method.
Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG)signal. Due to the simplicity and noninvasive nature, ECG signals are vastly used by physicians to determine the heart problems and abnormalities.
In this paper, a computer-aided diagnosis (CAD) system is provided for the automated classification and accurate diagnosis of seven types of cardiac arrhythmias using the ECG signal. The basis of this method is using machine learning algorithms to classify normal rhythm and six abnormal cardiac functions. In the proposed method, after the pre-processing stage, the ECG signal is segmented, and various morphological characteristics, frequency domain features, and nonlinear indices are extracted for the ECG signal. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are used on the combination of the extracted features in which, non-dominated sorting genetic algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include k-nearest neighbor (KNN), feed-forward neural network (FF net), fitting neural network (Fit net), radial basis function neural network (RBFNN) and pattern recognition network (Pat net) are employed for the classification. The highest accuracy obtained based on ten-fold cross-validation from the FF net is 98.75%, demonstrates the efficiency of the proposed method and the achieved improvement compared to the other similar works with the same dataset. The combination of a vast and various features from morphology, frequency, and nonlinear characteristics to demonstrate the diverse aspects of ECG signals as well as employing a multi-objective meta-heuristic optimization algorithm for selecting the more correlated features are the main contributions of this study. |
|---|---|
| AbstractList | •A new method for discrimination of seven types of ECG beats is proposed.•This method employs a combination of morphology, frequency, and nonlinear indices.•Application of feature selection (include GA, PSO, DE, and NSGA-II) is discussed.•KNN, RBFNN, FF-net, Fit-net, and Pat-net are employed for classification.•High classification accuracy 98.75% is obtained based on the proposed method.
Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG)signal. Due to the simplicity and noninvasive nature, ECG signals are vastly used by physicians to determine the heart problems and abnormalities.
In this paper, a computer-aided diagnosis (CAD) system is provided for the automated classification and accurate diagnosis of seven types of cardiac arrhythmias using the ECG signal. The basis of this method is using machine learning algorithms to classify normal rhythm and six abnormal cardiac functions. In the proposed method, after the pre-processing stage, the ECG signal is segmented, and various morphological characteristics, frequency domain features, and nonlinear indices are extracted for the ECG signal. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are used on the combination of the extracted features in which, non-dominated sorting genetic algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include k-nearest neighbor (KNN), feed-forward neural network (FF net), fitting neural network (Fit net), radial basis function neural network (RBFNN) and pattern recognition network (Pat net) are employed for the classification. The highest accuracy obtained based on ten-fold cross-validation from the FF net is 98.75%, demonstrates the efficiency of the proposed method and the achieved improvement compared to the other similar works with the same dataset. The combination of a vast and various features from morphology, frequency, and nonlinear characteristics to demonstrate the diverse aspects of ECG signals as well as employing a multi-objective meta-heuristic optimization algorithm for selecting the more correlated features are the main contributions of this study. Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG) signal. Due to the simplicity and noninvasive nature, ECG signals are vastly used by physicians to determine the heart problems and abnormalities. In this paper, a computer-aided diagnosis (CAD) system is provided for the automated classification and accurate diagnosis of seven types of cardiac arrhythmias using the ECG signal. The basis of this method is using machine learning algorithms to classify normal rhythm and six abnormal cardiac functions. In the proposed method, after the pre-processing stage, the ECG signal is segmented, and various morphological characteristics, frequency domain features, and nonlinear indices are extracted for the ECG signal. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are used on the combination of the extracted features in which, non-dominated sorting genetic algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include k-nearest neighbor (KNN), feed-forward neural network (FF net), fitting neural network (Fit net), radial basis function neural network (RBFNN) and pattern recognition network (Pat net) are employed for the classification. The highest accuracy obtained based on ten-fold cross-validation from the FF net is 98.75%, demonstrates the efficiency of the proposed method and the achieved improvement compared to the other similar works with the same dataset. The combination of a vast and various features from morphology, frequency, and nonlinear characteristics to demonstrate the diverse aspects of ECG signals as well as employing a multi-objective meta-heuristic optimization algorithm for selecting the more correlated features are the main contributions of this study. |
| ArticleNumber | 113697 |
| Author | Mazaheri, Vajihe Khodadadi, Hamed |
| Author_xml | – sequence: 1 givenname: Vajihe surname: Mazaheri fullname: Mazaheri, Vajihe email: vajihe.mazaheri@iaukhsh.ac.ir – sequence: 2 givenname: Hamed surname: Khodadadi fullname: Khodadadi, Hamed email: khodadadi@iaukhsh.ac.ir |
| BookMark | eNp9kcFu3CAURVGVSp2k-YGskLKtp4AdwFI31ShNKkXKpl0jDM_jN7LNFJhW8z390eKZdJNFVgh0z-O-ey_JxRxmIOSGszVnXH7erSH9sWvBRHngtWzVO7LiWtWVVG19QVasvVNVw1XzgVymtGOMK8bUivx9BBsztTEOxzxMaKlHu51DwkQ7m8DTMNM8AHVh6nC2Gcs99HQKcT-EMWzR2fET7SP8OsDsjtTOnhZzI85lMO3B5kOEtCD3mweacDvbMZ1UE2Q7wCFiyuj-K2mCEdzpFztuQ8Ri6iN53xcIrl_OK_Lz2_2PzWP19PzwffP1qXK10Lnq7J0U4LrWS64FV531zDHWC6mdU53WXnXMSad7KaEBgEY7rTtfW9G2vnf1Fbk9z93HULZJ2ezCIS5-jWgkl7xloikqfVa5GFKK0BuH-ZRLjhZHw5lZKjE7s1RilkrMuZKCilfoPuJk4_Ft6MsZgrL6b4RoksMSNXiMJSnjA76F_wNJTqx7 |
| CitedBy_id | crossref_primary_10_3390_asi6050095 crossref_primary_10_1080_10255842_2025_2456487 crossref_primary_10_3390_biomimetics8030310 crossref_primary_10_1002_cpe_6334 crossref_primary_10_35378_gujs_1507978 crossref_primary_10_1016_j_bspc_2021_103260 crossref_primary_10_1016_j_bspc_2024_106772 crossref_primary_10_1007_s10462_025_11266_y crossref_primary_10_1371_journal_pone_0322934 crossref_primary_10_1109_TIM_2024_3400302 crossref_primary_10_1007_s11277_022_09585_2 crossref_primary_10_1177_24056456241297300 crossref_primary_10_1016_j_cct_2021_106397 crossref_primary_10_1016_j_bspc_2022_104165 crossref_primary_10_1016_j_bspc_2022_104202 crossref_primary_10_1016_j_eswa_2021_115531 crossref_primary_10_1109_TFUZZ_2024_3416217 crossref_primary_10_1007_s11042_022_14227_7 crossref_primary_10_3390_agronomy13020402 crossref_primary_10_1038_s41598_025_93906_5 crossref_primary_10_3390_s21041511 crossref_primary_10_3390_math10020192 crossref_primary_10_1016_j_jksuci_2022_05_009 crossref_primary_10_1080_09540091_2021_2002266 crossref_primary_10_1016_j_ins_2021_03_062 crossref_primary_10_1080_10255842_2024_2332942 crossref_primary_10_1007_s00500_023_09062_3 crossref_primary_10_1016_j_eswa_2021_115688 crossref_primary_10_1016_j_bbe_2022_05_006 crossref_primary_10_1016_j_bspc_2022_103639 crossref_primary_10_1007_s40747_024_01419_x crossref_primary_10_1016_j_compbiomed_2023_107025 crossref_primary_10_1016_j_smhl_2024_100446 crossref_primary_10_1016_j_compbiomed_2025_110966 crossref_primary_10_1016_j_eswa_2023_121497 crossref_primary_10_1016_j_heliyon_2023_e13601 crossref_primary_10_1155_2022_2898061 crossref_primary_10_1049_htl2_70015 crossref_primary_10_1016_j_jksuci_2024_102096 crossref_primary_10_1016_j_envpol_2023_122564 crossref_primary_10_1109_JBHI_2021_3085318 crossref_primary_10_1016_j_engappai_2023_106839 crossref_primary_10_1038_s41598_025_86438_5 crossref_primary_10_1016_j_heliyon_2024_e26147 crossref_primary_10_1016_j_knosys_2021_107473 crossref_primary_10_1016_j_bspc_2022_104300 crossref_primary_10_1109_TIM_2024_3420364 crossref_primary_10_1016_j_bspc_2023_104718 crossref_primary_10_1016_j_eswa_2021_115271 crossref_primary_10_1016_j_eswa_2023_120019 crossref_primary_10_3389_fninf_2022_1052868 crossref_primary_10_34248_bsengineering_1566475 |
| Cites_doi | 10.1016/j.cmpb.2010.07.011 10.1016/j.ins.2017.06.027 10.1016/j.compbiomed.2018.03.016 10.1016/j.asoc.2015.07.010 10.1007/s40846-017-0235-3 10.1109/TBME.2011.2171037 10.1055/s-0038-1667083 10.1117/1.JMM.16.3.034505 10.1007/978-3-319-65981-7_3 10.1016/j.procs.2013.05.405 10.1109/TBME.2012.2191407 10.1007/s00521-018-03980-2 10.1109/CJECE.2016.2586939 10.1016/j.swevo.2017.10.002 10.11648/j.cbb.20160404.11 10.1016/j.eswa.2017.09.022 10.1016/j.knosys.2019.104923 10.1515/slgr-2015-0039 10.1016/j.cmpb.2015.12.008 10.1016/j.compbiomed.2018.06.002 10.1016/j.compbiomed.2017.09.011 10.1016/j.compbiomed.2017.08.022 10.1109/CIBCB.2010.5510702 10.1016/j.eswa.2007.12.016 10.1016/j.compbiomed.2018.09.009 10.3390/s18072090 10.1049/iet-syb.2018.5020 10.1109/TBME.2004.827359 10.1016/j.cmpb.2011.10.002 10.1109/JBHI.2014.2332001 10.1016/j.ins.2016.10.013 10.1016/j.neucom.2011.10.045 10.1007/s11045-016-0446-8 10.1016/j.compbiomed.2018.08.003 10.1109/JTEHM.2018.2878000 10.1016/j.eswa.2018.07.030 10.1016/j.bspc.2014.10.013 10.1016/j.bspc.2011.07.001 10.1049/iet-syb.2018.5130 10.1186/1475-925X-13-90 10.1016/j.bbe.2018.04.004 10.1016/j.cmpb.2019.104992 10.1109/TBME.2012.2213253 10.1016/j.cmpb.2015.12.024 10.1016/j.compbiomed.2013.11.019 10.1007/s11760-018-1237-5 10.18201/ijisae.2018637929 10.1016/j.bbe.2020.02.007 10.1109/TBME.2011.2113395 10.3390/s19235079 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Ltd Copyright Elsevier BV Dec 15, 2020 |
| Copyright_xml | – notice: 2020 Elsevier Ltd – notice: Copyright Elsevier BV Dec 15, 2020 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2020.113697 |
| 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 | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2020_113697 S0957417420305212 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ XPP ZMT ~HD 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c328t-ba562ecb9d618217bad0c00f268cc7b88d7b0c6c8f66e4eee48c88bd3a299dfc3 |
| ISICitedReferencesCount | 55 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000576781400020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sun Nov 30 05:18:21 EST 2025 Sat Nov 29 07:06:02 EST 2025 Tue Nov 18 21:45:04 EST 2025 Fri Feb 23 02:47:29 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Meta-heuristic optimization algorithm Cardiac arrhythmia recognition ECG signal FF net classifier Nonlinear indices |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-ba562ecb9d618217bad0c00f268cc7b88d7b0c6c8f66e4eee48c88bd3a299dfc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2461619024 |
| PQPubID | 2045477 |
| ParticipantIDs | proquest_journals_2461619024 crossref_citationtrail_10_1016_j_eswa_2020_113697 crossref_primary_10_1016_j_eswa_2020_113697 elsevier_sciencedirect_doi_10_1016_j_eswa_2020_113697 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-12-15 |
| PublicationDateYYYYMMDD | 2020-12-15 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-12-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Ganapathy, Swaminathan, Deserno (b0120) 2018; 27 Golrizkhatami, Acan (b0125) 2018; 114 De Lannoy, François, Delbeke, Verleysen (b0095) 2010 Escalona-Morán, Soriano, Fischer, Mirasso (b0115) 2014; 19 Kiani Boroujeni, Rastegari, Khodadadi (b0055) 2019; 13 Elhaj, Salim, Harris, Swee, Ahmed (b0110) 2016; 127 Zomorodi-moghadam, Abdar, Davarzani, Zhou, Pławiak, Acharya (b0315) 2019 Bazi, Alajlan, AlHichri, Malek (b0050) 2013 Das, S., and M. Chakraborty (2011), Comparison of power Spectral Density (pSD) of normal and Abnormal ECGs, paper presented at IJCA, Special Issue on 2nd National Conference-Computing, Communication and Sensor Network, (2): pp-10-14. Pławiak (b0240) 2018; 39 Khodadadi, Khaki-Sedigh, Ataei, Jahed-Motlagh, Hekmatnia (b0170) 2017; 37 Park, Cho, Lee, Song, Lee, Chee, Kim (b0225) 2008 Andreotti, Carr, Pimentel, Mahdi, De Vos (b0040) 2017; 44 De Chazal, O'Dwyer, Reilly (b0090) 2004; 51 Kumar, Pachori, Acharya (b0180) 2018; 38 Dilmac, Korurek (b0105) 2015; 36 Jung, Zscheischler (b0155) 2013; 18 Alvarado, Lakshminarayan, Principe (b0035) 2012; 59 Pasolli, Melgani (b0230) 2015; 19 Acharya, Krishnan, Spaan, Suri (b0010) 2007 Wang, Chiang, Hsu, Yang (b0275) 2013; 116 Chen, Wang, Wang (b0075) 2018; 16 Kora, P., A. Annavarapu, and S. Borra (2018), ECG based myocardial infarction detection using different classification techniques, in Classification in BioApps, edited, pp. 57-77, Springer. Rahman, Karim, Al Mahmud, Rahman (b0255) 2016; 4 Zhang, Dong, Luo, Choi, Wu (b0310) 2014; 46 Vivekanandan, Iyengar (b0270) 2017; 90 Kutlu, Kuntalp (b0185) 2012; 105 Mosbah, El-Hawary (b0210) 2016; 39 Acharya, Fujita, Adam, Lih, Sudarshan, Hong, Poo (b0025) 2017; 377 Bassiouni, El-Dahshan, Khalefa, Salem (b0045) 2018; 12 De Lannoy, François, Delbeke, Verleysen (b0100) 2011; 59 Mohebbi, Ghassemian (b0205) 2012; 105 Acharya, Fujita, Oh, Hagiwara, Tan, Adam (b0015) 2017; 415 Ceylan (b0070) 2018; 6 Hernandez-Matamoros, A., H. Fujita, E. Escamilla-Hernandez, H. Perez-Meana, and M. Nakano-Miyatake (2020), Recognition of ECG signals using wavelet based on atomic functions, Biocybernetics and Biomedical Engineering. Yu, Chou (b0300) 2009; 36 Mar, Zaunseder, Martínez, Llamedo, Poll (b0200) 2011; 58 Oh, Ng, San Tan, Acharya (b0220) 2018; 102 Nabian, Yin, Wormwood, Quigley, Barrett, Ostadabbas (b0215) 2018; 6 Pławiak, P., and U. R. Acharya (2019, Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals, Neural Computing and Applications, 1-25. Soria, M. L., and J. Martínez (2009), Analysis of multidomain features for ECG classification, paper presented at 2009 36th Annual Computers in Cardiology Conference (CinC), IEEE. Daamouche, Hamami, Alajlan, Melgani (b0080) 2012; 7 Acharya, Oh, Hagiwara, Tan, Adam, Gertych, San Tan (b0020) 2017; 89 Ahmad, Matti, EssaO, ALhabib, Shaikhow (b0030) 2018; 9 Pławiak (b0235) 2018; 92 Huang, Liu, Zhu, Wang, Hu (b0150) 2014; 13 Hajeb-Mohammadalipour, Ahmadi, Shahghadami, Chon (b0135) 2018; 18 Yıldırım, Pławiak, Tan, Acharya (b0295) 2018; 102 Bouaziz, Boutana, Oulhadj (b0065) 2018 Hassanien, A. E., M. Kilany, and E. H. Houssein (2018), Combining support vector machine and elephant herding optimization for cardiac arrhythmias, arXiv preprint arXiv:1806.08242. Kandala, Dhuli, Pławiak, Naik, Moeinzadeh, Gargiulo, Gunnam (b0160) 2019; 19 Luz, E. J. d. S., W. R. Schwartz, G. Cámara-Chávez, and D. Menotti (2016), ECG-based heartbeat classification for arrhythmia detection: A survey, Computer methods and programs in biomedicine, 127, 144-164. Pourbabaee, B., and C. Lucas (2010), Paroxysmal atrial fibrillation diagnosis based on feature extraction and classification, paper presented at 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, IEEE. Yang, Si, Wang, Guo (b0280) 2018; 101 Borowska (b0060) 2015; 43 Arab Zade, Khodadadi (b0305) 2018; 13 Tuncer, Dogan, Pławiak, Acharya (b0265) 2019; 186 Lutich (b0190) 2017; 16 Abdar, Książek, Acharya, Tan, Makarenkov, Pławiak (b0005) 2019; 179 Khodadadi, Khaki-Sedigh, Ataei, Jahed-Motlagh (b0165) 2018; 29 Goodfellow, I., Y. Bengio, A. Courville, and Y. Bengio (2016), Deep learning, MIT press Cambridge. Yildirim (b0290) 2018; 96 Ye, Kumar, Coimbra (b0285) 2012; 59 Acharya (10.1016/j.eswa.2020.113697_b0025) 2017; 377 De Lannoy (10.1016/j.eswa.2020.113697_b0095) 2010 De Chazal (10.1016/j.eswa.2020.113697_b0090) 2004; 51 Kutlu (10.1016/j.eswa.2020.113697_b0185) 2012; 105 Acharya (10.1016/j.eswa.2020.113697_b0015) 2017; 415 Chen (10.1016/j.eswa.2020.113697_b0075) 2018; 16 Huang (10.1016/j.eswa.2020.113697_b0150) 2014; 13 10.1016/j.eswa.2020.113697_b0195 10.1016/j.eswa.2020.113697_b0145 Pasolli (10.1016/j.eswa.2020.113697_b0230) 2015; 19 Rahman (10.1016/j.eswa.2020.113697_b0255) 2016; 4 Bassiouni (10.1016/j.eswa.2020.113697_b0045) 2018; 12 Ganapathy (10.1016/j.eswa.2020.113697_b0120) 2018; 27 Daamouche (10.1016/j.eswa.2020.113697_b0080) 2012; 7 Elhaj (10.1016/j.eswa.2020.113697_b0110) 2016; 127 Yildirim (10.1016/j.eswa.2020.113697_b0290) 2018; 96 Mar (10.1016/j.eswa.2020.113697_b0200) 2011; 58 Kumar (10.1016/j.eswa.2020.113697_b0180) 2018; 38 Mohebbi (10.1016/j.eswa.2020.113697_b0205) 2012; 105 Jung (10.1016/j.eswa.2020.113697_b0155) 2013; 18 Abdar (10.1016/j.eswa.2020.113697_b0005) 2019; 179 Ahmad (10.1016/j.eswa.2020.113697_b0030) 2018; 9 Lutich (10.1016/j.eswa.2020.113697_b0190) 2017; 16 Nabian (10.1016/j.eswa.2020.113697_b0215) 2018; 6 De Lannoy (10.1016/j.eswa.2020.113697_b0100) 2011; 59 10.1016/j.eswa.2020.113697_b0260 10.1016/j.eswa.2020.113697_b0140 Zhang (10.1016/j.eswa.2020.113697_b0310) 2014; 46 Khodadadi (10.1016/j.eswa.2020.113697_b0165) 2018; 29 Ye (10.1016/j.eswa.2020.113697_b0285) 2012; 59 Escalona-Morán (10.1016/j.eswa.2020.113697_b0115) 2014; 19 Ceylan (10.1016/j.eswa.2020.113697_b0070) 2018; 6 Yıldırım (10.1016/j.eswa.2020.113697_b0295) 2018; 102 Dilmac (10.1016/j.eswa.2020.113697_b0105) 2015; 36 Yang (10.1016/j.eswa.2020.113697_b0280) 2018; 101 10.1016/j.eswa.2020.113697_b0130 10.1016/j.eswa.2020.113697_b0175 Bazi (10.1016/j.eswa.2020.113697_b0050) 2013 Kiani Boroujeni (10.1016/j.eswa.2020.113697_b0055) 2019; 13 Vivekanandan (10.1016/j.eswa.2020.113697_b0270) 2017; 90 Wang (10.1016/j.eswa.2020.113697_b0275) 2013; 116 10.1016/j.eswa.2020.113697_b0250 10.1016/j.eswa.2020.113697_b0245 Borowska (10.1016/j.eswa.2020.113697_b0060) 2015; 43 Khodadadi (10.1016/j.eswa.2020.113697_b0170) 2017; 37 Pławiak (10.1016/j.eswa.2020.113697_b0240) 2018; 39 Bouaziz (10.1016/j.eswa.2020.113697_b0065) 2018 Alvarado (10.1016/j.eswa.2020.113697_b0035) 2012; 59 Acharya (10.1016/j.eswa.2020.113697_b0020) 2017; 89 Golrizkhatami (10.1016/j.eswa.2020.113697_b0125) 2018; 114 Andreotti (10.1016/j.eswa.2020.113697_b0040) 2017; 44 Acharya (10.1016/j.eswa.2020.113697_b0010) 2007 Zomorodi-moghadam (10.1016/j.eswa.2020.113697_b0315) 2019 Pławiak (10.1016/j.eswa.2020.113697_b0235) 2018; 92 Yu (10.1016/j.eswa.2020.113697_b0300) 2009; 36 10.1016/j.eswa.2020.113697_b0085 Mosbah (10.1016/j.eswa.2020.113697_b0210) 2016; 39 Hajeb-Mohammadalipour (10.1016/j.eswa.2020.113697_b0135) 2018; 18 Kandala (10.1016/j.eswa.2020.113697_b0160) 2019; 19 Oh (10.1016/j.eswa.2020.113697_b0220) 2018; 102 Tuncer (10.1016/j.eswa.2020.113697_b0265) 2019; 186 Park (10.1016/j.eswa.2020.113697_b0225) 2008 Arab Zade (10.1016/j.eswa.2020.113697_b0305) 2018; 13 |
| References_xml | – year: 2013 ident: b0050 article-title: Domain adaptation methods for ECG classification – volume: 59 start-page: 241 year: 2011 end-page: 247 ident: b0100 article-title: Weighted conditional random fields for supervised interpatient heartbeat classification publication-title: IEEE Transactions on Biomedical Engineering – volume: 415 start-page: 190 year: 2017 end-page: 198 ident: b0015 article-title: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals publication-title: Information Sciences – volume: 101 start-page: 22 year: 2018 end-page: 32 ident: b0280 article-title: Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine publication-title: Computers in Biology and Medicine – volume: 13 start-page: 1 year: 2018 end-page: 7 ident: b0305 article-title: Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms publication-title: IET Systems Biology – volume: 16 start-page: 131 year: 2018 end-page: 138 ident: b0075 article-title: Arrhythmia recognition and classification using ECG morphology and segment feature analysis publication-title: IEEE/ACM Transactions on Computational Biology and Bioinformatics – volume: 19 start-page: 5079 year: 2019 ident: b0160 article-title: Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method publication-title: Sensors – volume: 6 start-page: 1 year: 2018 end-page: 11 ident: b0215 article-title: An open-source feature extraction tool for the analysis of peripheral physiological data publication-title: IEEE Journal of Translational Engineering in Health and Medicine – reference: Soria, M. L., and J. Martínez (2009), Analysis of multidomain features for ECG classification, paper presented at 2009 36th Annual Computers in Cardiology Conference (CinC), IEEE. – volume: 36 start-page: 641 year: 2015 end-page: 655 ident: b0105 article-title: ECG heart beat classification method based on modified ABC algorithm publication-title: Applied Soft Computing – year: 2019 ident: b0315 article-title: Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease publication-title: Expert Systems – volume: 43 start-page: 21 year: 2015 end-page: 32 ident: b0060 article-title: Entropy-based algorithms in the analysis of biomedical signals publication-title: Studies in Logic, Grammar and Rhetoric – year: 2018 ident: b0065 article-title: Diagnostic of ECG Arrhythmia using Wavelet Analysis and K-Nearest Neighbor – reference: Luz, E. J. d. S., W. R. Schwartz, G. Cámara-Chávez, and D. Menotti (2016), ECG-based heartbeat classification for arrhythmia detection: A survey, Computer methods and programs in biomedicine, 127, 144-164. – reference: Goodfellow, I., Y. Bengio, A. Courville, and Y. Bengio (2016), Deep learning, MIT press Cambridge. – volume: 186 year: 2019 ident: b0265 article-title: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals publication-title: Knowledge-Based Systems – year: 2010 ident: b0095 article-title: Weighted SVMs and feature relevance assessment in supervised heart beat classification, paper presented at International Joint Conference on Biomedical Engineering Systems and Technologies – volume: 19 start-page: 130 year: 2015 end-page: 136 ident: b0230 article-title: Genetic algorithm-based method for mitigating label noise issue in ECG signal classification publication-title: Biomedical Signal Processing and Control – reference: Pławiak, P., and U. R. Acharya (2019, Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals, Neural Computing and Applications, 1-25. – reference: Hernandez-Matamoros, A., H. Fujita, E. Escamilla-Hernandez, H. Perez-Meana, and M. Nakano-Miyatake (2020), Recognition of ECG signals using wavelet based on atomic functions, Biocybernetics and Biomedical Engineering. – volume: 377 start-page: 17 year: 2017 end-page: 29 ident: b0025 article-title: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study publication-title: Information Sciences – reference: Das, S., and M. Chakraborty (2011), Comparison of power Spectral Density (pSD) of normal and Abnormal ECGs, paper presented at IJCA, Special Issue on 2nd National Conference-Computing, Communication and Sensor Network, (2): pp-10-14. – volume: 18 start-page: 2337 year: 2013 end-page: 2346 ident: b0155 article-title: A guided hybrid genetic algorithm for feature selection with expensive cost functions publication-title: Procedia Computer Science – volume: 36 start-page: 2088 year: 2009 end-page: 2096 ident: b0300 article-title: Selection of significant independent components for ECG beat classification publication-title: Expert Systems with Applications – year: 2008 ident: b0225 article-title: Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis – volume: 89 start-page: 389 year: 2017 end-page: 396 ident: b0020 article-title: A deep convolutional neural network model to classify heartbeats publication-title: Computers in Biology and Medicine – volume: 114 start-page: 54 year: 2018 end-page: 64 ident: b0125 article-title: ECG classification using three-level fusion of different feature descriptors publication-title: Expert Systems with Applications – volume: 18 start-page: 2090 year: 2018 ident: b0135 article-title: Automated method for discrimination of arrhythmias using time, frequency, and nonlinear features of electrocardiogram signals publication-title: Sensors – volume: 13 start-page: 90 year: 2014 ident: b0150 article-title: A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals publication-title: Biomedical Engineering Online – volume: 39 start-page: 283 year: 2016 end-page: 291 ident: b0210 article-title: Hourly electricity price forecasting for the next month using multilayer neural network publication-title: Canadian Journal of Electrical and Computer Engineering – volume: 90 start-page: 125 year: 2017 end-page: 136 ident: b0270 article-title: Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease publication-title: Computers in Biology and Medicine – volume: 59 start-page: 2930 year: 2012 end-page: 2941 ident: b0285 article-title: Heartbeat classification using morphological and dynamic features of ECG signals publication-title: IEEE Transactions on Biomedical Engineering – volume: 51 start-page: 1196 year: 2004 end-page: 1206 ident: b0090 article-title: Automatic classification of heartbeats using ECG morphology and heartbeat interval features publication-title: IEEE Transactions on Biomedical Engineering – volume: 105 start-page: 257 year: 2012 end-page: 267 ident: b0185 article-title: Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients publication-title: Computer Methods and Programs in Biomedicine – volume: 102 start-page: 278 year: 2018 end-page: 287 ident: b0220 article-title: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats publication-title: Computers in Biology and Medicine – volume: 29 start-page: 19 year: 2018 end-page: 33 ident: b0165 article-title: Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: The case of breast mammograms publication-title: Multidimensional Systems and Signal Processing – volume: 127 start-page: 52 year: 2016 end-page: 63 ident: b0110 article-title: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals publication-title: Computer Methods and Programs in Biomedicine – volume: 58 start-page: 2168 year: 2011 end-page: 2177 ident: b0200 article-title: Optimization of ECG classification by means of feature selection publication-title: IEEE Transactions on Biomedical Engineering – volume: 96 start-page: 189 year: 2018 end-page: 202 ident: b0290 article-title: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification publication-title: Computers in Biology and Medicine – volume: 46 start-page: 79 year: 2014 end-page: 89 ident: b0310 article-title: Heartbeat classification using disease-specific feature selection publication-title: Computers in Biology and Medicine – volume: 4 start-page: 27 year: 2016 end-page: 36 ident: b0255 article-title: Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions publication-title: Computational Biology and Bioinformatics – volume: 92 start-page: 334 year: 2018 end-page: 349 ident: b0235 article-title: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system publication-title: Expert Systems with Applications – volume: 38 start-page: 564 year: 2018 end-page: 573 ident: b0180 article-title: Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform publication-title: Biocybernetics and Biomedical Engineering – volume: 39 start-page: 192 year: 2018 end-page: 208 ident: b0240 article-title: Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals publication-title: Swarm and evolutionary computation – volume: 9 start-page: 383 year: 2018 end-page: 389 ident: b0030 article-title: Features optimization for ECG signals classification publication-title: Int. J. Adv. Comput. Sci Appl. – volume: 12 start-page: 941 year: 2018 end-page: 949 ident: b0045 article-title: Intelligent hybrid approaches for human ECG signals identification publication-title: Signal, Image and Video Processing – volume: 116 start-page: 38 year: 2013 end-page: 45 ident: b0275 article-title: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method publication-title: Neurocomputing – volume: 27 start-page: 098 year: 2018 end-page: 109 ident: b0120 article-title: Deep learning on 1-D biosignals: A taxonomy-based survey publication-title: Yearbook of Medical Informatics – volume: 179 year: 2019 ident: b0005 article-title: A new machine learning technique for an accurate diagnosis of coronary artery disease publication-title: Computer methods and programs in biomedicine – volume: 13 start-page: 260 year: 2019 end-page: 266 ident: b0055 article-title: diagnosis of attention deficit hyperactivity disorder using nonlinear analysis of the EEG signal publication-title: IET Systems Biology – reference: Pourbabaee, B., and C. Lucas (2010), Paroxysmal atrial fibrillation diagnosis based on feature extraction and classification, paper presented at 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, IEEE. – volume: 105 start-page: 40 year: 2012 end-page: 49 ident: b0205 article-title: Prediction of paroxysmal atrial fibrillation based on nonlinear analysis and spectrum and bispectrum features of the heart rate variability signal publication-title: Computer Methods and Programs in Biomedicine – volume: 7 start-page: 342 year: 2012 end-page: 349 ident: b0080 article-title: A wavelet optimization approach for ECG signal classification publication-title: Biomedical Signal Processing and Control – volume: 44 start-page: 1 year: 2017 ident: b0040 article-title: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG publication-title: Computing – reference: Hassanien, A. E., M. Kilany, and E. H. Houssein (2018), Combining support vector machine and elephant herding optimization for cardiac arrhythmias, arXiv preprint arXiv:1806.08242. – year: 2007 ident: b0010 article-title: Advances in cardiac signal processing – volume: 102 start-page: 411 year: 2018 end-page: 420 ident: b0295 article-title: Arrhythmia detection using deep convolutional neural network with long duration ECG signals publication-title: Computers in Biology and Medicine – volume: 59 start-page: 1641 year: 2012 end-page: 1648 ident: b0035 article-title: Time-based compression and classification of heartbeats publication-title: IEEE Trans. Biomed. Eng. – volume: 6 start-page: 40 year: 2018 end-page: 46 ident: b0070 article-title: The effect of feature extraction based on dictionary learning on ECG signal classification publication-title: International Journal of Intelligent Systems and Applications in Engineering – volume: 37 start-page: 409 year: 2017 end-page: 419 ident: b0170 article-title: Nonlinear analysis of the contour boundary irregularity of skin lesion using Lyapunov exponent and KS entropy publication-title: Journal of Medical and Biological Engineering – volume: 16 year: 2017 ident: b0190 article-title: PatterNet: A system to learn compact physical design pattern representations for pattern-based analytics publication-title: Journal of Micro/Nanolithography, MEMS, and MOEMS – reference: Kora, P., A. Annavarapu, and S. Borra (2018), ECG based myocardial infarction detection using different classification techniques, in Classification in BioApps, edited, pp. 57-77, Springer. – volume: 19 start-page: 892 year: 2014 end-page: 898 ident: b0115 article-title: Electrocardiogram classification using reservoir computing with logistic regression publication-title: IEEE Journal of Biomedical and health Informatics – volume: 105 start-page: 40 issue: 1 year: 2012 ident: 10.1016/j.eswa.2020.113697_b0205 article-title: Prediction of paroxysmal atrial fibrillation based on nonlinear analysis and spectrum and bispectrum features of the heart rate variability signal publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2010.07.011 – volume: 415 start-page: 190 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0015 article-title: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals publication-title: Information Sciences doi: 10.1016/j.ins.2017.06.027 – volume: 96 start-page: 189 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0290 article-title: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.03.016 – volume: 36 start-page: 641 year: 2015 ident: 10.1016/j.eswa.2020.113697_b0105 article-title: ECG heart beat classification method based on modified ABC algorithm publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.07.010 – volume: 9 start-page: 383 issue: 11 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0030 article-title: Features optimization for ECG signals classification publication-title: Int. J. Adv. Comput. Sci Appl. – year: 2013 ident: 10.1016/j.eswa.2020.113697_b0050 – volume: 37 start-page: 409 issue: 3 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0170 article-title: Nonlinear analysis of the contour boundary irregularity of skin lesion using Lyapunov exponent and KS entropy publication-title: Journal of Medical and Biological Engineering doi: 10.1007/s40846-017-0235-3 – ident: 10.1016/j.eswa.2020.113697_b0085 – volume: 16 start-page: 131 issue: 1 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0075 article-title: Arrhythmia recognition and classification using ECG morphology and segment feature analysis publication-title: IEEE/ACM Transactions on Computational Biology and Bioinformatics – volume: 59 start-page: 241 issue: 1 year: 2011 ident: 10.1016/j.eswa.2020.113697_b0100 article-title: Weighted conditional random fields for supervised interpatient heartbeat classification publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2011.2171037 – year: 2018 ident: 10.1016/j.eswa.2020.113697_b0065 – volume: 27 start-page: 098 issue: 01 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0120 article-title: Deep learning on 1-D biosignals: A taxonomy-based survey publication-title: Yearbook of Medical Informatics doi: 10.1055/s-0038-1667083 – volume: 16 issue: 3 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0190 article-title: PatterNet: A system to learn compact physical design pattern representations for pattern-based analytics publication-title: Journal of Micro/Nanolithography, MEMS, and MOEMS doi: 10.1117/1.JMM.16.3.034505 – ident: 10.1016/j.eswa.2020.113697_b0175 doi: 10.1007/978-3-319-65981-7_3 – volume: 18 start-page: 2337 year: 2013 ident: 10.1016/j.eswa.2020.113697_b0155 article-title: A guided hybrid genetic algorithm for feature selection with expensive cost functions publication-title: Procedia Computer Science doi: 10.1016/j.procs.2013.05.405 – volume: 59 start-page: 1641 issue: 6 year: 2012 ident: 10.1016/j.eswa.2020.113697_b0035 article-title: Time-based compression and classification of heartbeats publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2012.2191407 – ident: 10.1016/j.eswa.2020.113697_b0245 doi: 10.1007/s00521-018-03980-2 – volume: 39 start-page: 283 issue: 4 year: 2016 ident: 10.1016/j.eswa.2020.113697_b0210 article-title: Hourly electricity price forecasting for the next month using multilayer neural network publication-title: Canadian Journal of Electrical and Computer Engineering doi: 10.1109/CJECE.2016.2586939 – volume: 39 start-page: 192 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0240 article-title: Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals publication-title: Swarm and evolutionary computation doi: 10.1016/j.swevo.2017.10.002 – volume: 4 start-page: 27 year: 2016 ident: 10.1016/j.eswa.2020.113697_b0255 article-title: Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions publication-title: Computational Biology and Bioinformatics doi: 10.11648/j.cbb.20160404.11 – volume: 92 start-page: 334 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0235 article-title: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.09.022 – volume: 186 year: 2019 ident: 10.1016/j.eswa.2020.113697_b0265 article-title: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.104923 – volume: 43 start-page: 21 issue: 1 year: 2015 ident: 10.1016/j.eswa.2020.113697_b0060 article-title: Entropy-based algorithms in the analysis of biomedical signals publication-title: Studies in Logic, Grammar and Rhetoric doi: 10.1515/slgr-2015-0039 – ident: 10.1016/j.eswa.2020.113697_b0195 doi: 10.1016/j.cmpb.2015.12.008 – volume: 102 start-page: 278 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0220 article-title: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.06.002 – volume: 90 start-page: 125 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0270 article-title: Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2017.09.011 – volume: 89 start-page: 389 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0020 article-title: A deep convolutional neural network model to classify heartbeats publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2017.08.022 – ident: 10.1016/j.eswa.2020.113697_b0250 doi: 10.1109/CIBCB.2010.5510702 – volume: 36 start-page: 2088 issue: 2 year: 2009 ident: 10.1016/j.eswa.2020.113697_b0300 article-title: Selection of significant independent components for ECG beat classification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2007.12.016 – volume: 102 start-page: 411 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0295 article-title: Arrhythmia detection using deep convolutional neural network with long duration ECG signals publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.09.009 – volume: 18 start-page: 2090 issue: 7 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0135 article-title: Automated method for discrimination of arrhythmias using time, frequency, and nonlinear features of electrocardiogram signals publication-title: Sensors doi: 10.3390/s18072090 – volume: 13 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0305 article-title: Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms publication-title: IET Systems Biology doi: 10.1049/iet-syb.2018.5020 – year: 2010 ident: 10.1016/j.eswa.2020.113697_b0095 – volume: 51 start-page: 1196 issue: 7 year: 2004 ident: 10.1016/j.eswa.2020.113697_b0090 article-title: Automatic classification of heartbeats using ECG morphology and heartbeat interval features publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2004.827359 – volume: 105 start-page: 257 issue: 3 year: 2012 ident: 10.1016/j.eswa.2020.113697_b0185 article-title: Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2011.10.002 – volume: 44 start-page: 1 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0040 article-title: Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG publication-title: Computing – ident: 10.1016/j.eswa.2020.113697_b0140 – year: 2007 ident: 10.1016/j.eswa.2020.113697_b0010 – volume: 19 start-page: 892 issue: 3 year: 2014 ident: 10.1016/j.eswa.2020.113697_b0115 article-title: Electrocardiogram classification using reservoir computing with logistic regression publication-title: IEEE Journal of Biomedical and health Informatics doi: 10.1109/JBHI.2014.2332001 – year: 2019 ident: 10.1016/j.eswa.2020.113697_b0315 article-title: Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease publication-title: Expert Systems – volume: 377 start-page: 17 year: 2017 ident: 10.1016/j.eswa.2020.113697_b0025 article-title: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study publication-title: Information Sciences doi: 10.1016/j.ins.2016.10.013 – volume: 116 start-page: 38 year: 2013 ident: 10.1016/j.eswa.2020.113697_b0275 article-title: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.10.045 – volume: 29 start-page: 19 issue: 1 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0165 article-title: Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: The case of breast mammograms publication-title: Multidimensional Systems and Signal Processing doi: 10.1007/s11045-016-0446-8 – volume: 101 start-page: 22 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0280 article-title: Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.08.003 – volume: 6 start-page: 1 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0215 article-title: An open-source feature extraction tool for the analysis of peripheral physiological data publication-title: IEEE Journal of Translational Engineering in Health and Medicine doi: 10.1109/JTEHM.2018.2878000 – ident: 10.1016/j.eswa.2020.113697_b0260 – volume: 114 start-page: 54 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0125 article-title: ECG classification using three-level fusion of different feature descriptors publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.07.030 – volume: 19 start-page: 130 year: 2015 ident: 10.1016/j.eswa.2020.113697_b0230 article-title: Genetic algorithm-based method for mitigating label noise issue in ECG signal classification publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2014.10.013 – volume: 7 start-page: 342 issue: 4 year: 2012 ident: 10.1016/j.eswa.2020.113697_b0080 article-title: A wavelet optimization approach for ECG signal classification publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2011.07.001 – volume: 13 start-page: 260 issue: 5 year: 2019 ident: 10.1016/j.eswa.2020.113697_b0055 article-title: diagnosis of attention deficit hyperactivity disorder using nonlinear analysis of the EEG signal publication-title: IET Systems Biology doi: 10.1049/iet-syb.2018.5130 – volume: 13 start-page: 90 issue: 1 year: 2014 ident: 10.1016/j.eswa.2020.113697_b0150 article-title: A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals publication-title: Biomedical Engineering Online doi: 10.1186/1475-925X-13-90 – volume: 38 start-page: 564 issue: 3 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0180 article-title: Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform publication-title: Biocybernetics and Biomedical Engineering doi: 10.1016/j.bbe.2018.04.004 – volume: 179 year: 2019 ident: 10.1016/j.eswa.2020.113697_b0005 article-title: A new machine learning technique for an accurate diagnosis of coronary artery disease publication-title: Computer methods and programs in biomedicine doi: 10.1016/j.cmpb.2019.104992 – volume: 59 start-page: 2930 issue: 10 year: 2012 ident: 10.1016/j.eswa.2020.113697_b0285 article-title: Heartbeat classification using morphological and dynamic features of ECG signals publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2012.2213253 – volume: 127 start-page: 52 year: 2016 ident: 10.1016/j.eswa.2020.113697_b0110 article-title: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2015.12.024 – year: 2008 ident: 10.1016/j.eswa.2020.113697_b0225 – volume: 46 start-page: 79 year: 2014 ident: 10.1016/j.eswa.2020.113697_b0310 article-title: Heartbeat classification using disease-specific feature selection publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2013.11.019 – volume: 12 start-page: 941 issue: 5 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0045 article-title: Intelligent hybrid approaches for human ECG signals identification publication-title: Signal, Image and Video Processing doi: 10.1007/s11760-018-1237-5 – volume: 6 start-page: 40 issue: 1 year: 2018 ident: 10.1016/j.eswa.2020.113697_b0070 article-title: The effect of feature extraction based on dictionary learning on ECG signal classification publication-title: International Journal of Intelligent Systems and Applications in Engineering doi: 10.18201/ijisae.2018637929 – ident: 10.1016/j.eswa.2020.113697_b0130 – ident: 10.1016/j.eswa.2020.113697_b0145 doi: 10.1016/j.bbe.2020.02.007 – volume: 58 start-page: 2168 issue: 8 year: 2011 ident: 10.1016/j.eswa.2020.113697_b0200 article-title: Optimization of ECG classification by means of feature selection publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2011.2113395 – volume: 19 start-page: 5079 issue: 23 year: 2019 ident: 10.1016/j.eswa.2020.113697_b0160 article-title: Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method publication-title: Sensors doi: 10.3390/s19235079 |
| SSID | ssj0017007 |
| Score | 2.5347877 |
| Snippet | •A new method for discrimination of seven types of ECG beats is proposed.•This method employs a combination of morphology, frequency, and nonlinear... Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 113697 |
| SubjectTerms | Abnormalities Arrhythmia Cardiac arrhythmia Cardiac arrhythmia recognition Classification Diagnosis ECG signal Electrocardiography Feature extraction FF net classifier Genetic algorithms Heart diseases Heuristic methods Machine learning Meta-heuristic optimization algorithm Morphology Multiple objective analysis Neural networks Nonlinear indices Optimization Optimization algorithms Pattern recognition Physicians Radial basis function Signal processing Sorting algorithms |
| Title | Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm |
| URI | https://dx.doi.org/10.1016/j.eswa.2020.113697 https://www.proquest.com/docview/2461619024 |
| Volume | 161 |
| WOSCitedRecordID | wos000576781400020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLdKx4EL32gbA_nALQQ5H42d4zQVBkgTh4F6i2zHIa3aZkqzsfHfIP5RnuOPrkWb2AFVsqro2XL8frGf7fd-D6E3VZaWVULBcpNQpKSKQgFWPux5UlLmQkVixPtkE_TkhE0m-ZfB4JeLhbmY0-WSXV7mZ_9V1fAMlK1DZ--gbt8oPID_oHQoQe1Q_pPij-GFuoC3bX3V1Ysp18er2ptuugr0klXa6wHtTA67Ym8xLhoYcTcT6oGvWuNlbQialoZSg7dBpXou0N4FZHz0IdAeIJqDWUstVMdrdW7Yn51ksOpz7fR-z_PvTTuFbm1cCGi25c5ySrtou2v36utD85-8tpHx3_hsWntMfq6bksPPZODmCxuwZQ8z4t4xxIRz-lNJGqaRSdzjJ2hD126nWJ2Exrj0_jX7m4OI2Tu1-qEppWKTscYIb1Jtby2B3jHR-bzNCt1GodsoTBv30E5MRzkbop3Dj-PJJ39VRYmJyXc9t5FZxolwuyc3WT9bdkBv3Jw-Rg_trgQfGjQ9QQO1fIoeuYwf2C4Az9DvHlx4DS7swYV7cOFmiQFc-Bq4cFPhDXC9xR5aGECDPbSwg5auAtDCFlq91Aa0nCT20MIeWs_R1_fj06Pj0Kb5CGUSsy4UHGxwJUVeZrDZjajgJZGEVHHGpKSCsZIKIjPJqixTqVIqZZIxUSYcTKmykskLNISeql2Eo0ykmg6KCFKliiYiI5RyInWCdZrLZA9FbvgLaTnwdSqWeXGz4vdQ4OucGQaYW6VHTquFtWGNbVoASG-td-AgUNjJZFVorscMLPY43b9TJ16iB-uP6wANu_ZcvUL35UU3XbWvLYD_AP2Z0Ok |
| linkProvider | Elsevier |
| 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=Heart+arrhythmia+diagnosis+based+on+the+combination+of+morphological%2C+frequency+and+nonlinear+features+of+ECG+signals+and+metaheuristic+feature+selection+algorithm&rft.jtitle=Expert+systems+with+applications&rft.au=Mazaheri%2C+Vajihe&rft.au=Khodadadi%2C+Hamed&rft.date=2020-12-15&rft.issn=0957-4174&rft.volume=161&rft.spage=113697&rft_id=info:doi/10.1016%2Fj.eswa.2020.113697&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2020_113697 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |