Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads

Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence,...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Knowledge-based systems Jg. 99; S. 146 - 156
Hauptverfasser:	Acharya, U. Rajendra, Fujita, Hamido, Sudarshan, Vidya K., Oh, Shu Lih, Adam, Muhammad, Koh, Joel E.W., Tan, Jen Hong, Ghista, Dhanjoo N., Martis, Roshan Joy, Chua, Chua K., Poo, Chua Kok, Tan, Ru San
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2016
Schlagworte:	Automation Classification Classifier Computer programs Discrete wavelet transform Electrocardiogram Entropy Myocardial infarction Nonlinearity Position (location) Software Myocardial infarction Entropy Discrete wavelet transform Classifier Electrocardiogram
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy (Eax), signal energy (Ωx), fuzzy entropy (Efx), Kolmogorov–Sinai entropy (Eksx), permutation entropy (Epx), Renyi entropy (Erx), Shannon entropy (Eshx), Tsallis entropy (Etsx), wavelet entropy (Ewx), fractal dimension (FDx), Kolmogorov complexity (Ckx), and largest Lyapunov exponent (ELLEx) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.
AbstractList	Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy (Eax), signal energy (Ωx), fuzzy entropy (Efx), Kolmogorov–Sinai entropy (Eksx), permutation entropy (Epx), Renyi entropy (Erx), Shannon entropy (Eshx), Tsallis entropy (Etsx), wavelet entropy (Ewx), fractal dimension (FDx), Kolmogorov complexity (Ckx), and largest Lyapunov exponent (ELLEx) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision. Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy (View the MathML sourceEax), signal energy ( Omega super(x) ), fuzzy entropy (View the MathML sourceEfx), Kolmogorov-Sinai entropy (View the MathML sourceEksx), permutation entropy (View the MathML sourceEpx), Renyi entropy (View the MathML sourceErx), Shannon entropy (View the MathML sourceEshx), Tsallis entropy (View the MathML sourceEtsx), wavelet entropy (View the MathML sourceEwx), fractal dimension (View the MathML sourceFDx), Kolmogorov complexity (View the MathML sourceCkx), and largest Lyapunov exponent (View the MathML sourceELLEx) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V sub(5)). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V sub(3)). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V sub(3)) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.
Author	Oh, Shu Lih Martis, Roshan Joy Chua, Chua K. Tan, Jen Hong Fujita, Hamido Adam, Muhammad Tan, Ru San Acharya, U. Rajendra Sudarshan, Vidya K. Koh, Joel E.W. Ghista, Dhanjoo N. Poo, Chua Kok
Author_xml	– sequence: 1 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 2 givenname: Hamido surname: Fujita fullname: Fujita, Hamido organization: Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japan – sequence: 3 givenname: Vidya K. surname: Sudarshan fullname: Sudarshan, Vidya K. email: vidya.2kus@gmail.com organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 4 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 5 givenname: Muhammad surname: Adam fullname: Adam, Muhammad organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 6 givenname: Joel E.W. surname: Koh fullname: Koh, Joel E.W. organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 7 givenname: Jen Hong surname: Tan fullname: Tan, Jen Hong organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 8 givenname: Dhanjoo N. surname: Ghista fullname: Ghista, Dhanjoo N. organization: University 2020 Foundation, MA, USA – sequence: 9 givenname: Roshan Joy surname: Martis fullname: Martis, Roshan Joy organization: Department of Electronics and Communication Engineering, St. Joseph Engineering College, Mangalore, India – sequence: 10 givenname: Chua K. surname: Chua fullname: Chua, Chua K. organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 11 givenname: Chua Kok surname: Poo fullname: Poo, Chua Kok organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 12 givenname: Ru San surname: Tan fullname: Tan, Ru San organization: Department of Cardiology, National Heart Centre, Singapore
BookMark	eNqFkE1r3DAQhkVJIZuk_yAHHXuxO7LlrxwKIfQjEOilOYuRNA7a2tJWkgNbyH-vN-6ph_Y0zMz7vIfngp354ImxawGlANF-2Jc_fEjHVFbrVoIoQcIbthN9VxWdhOGM7WBooOigEefsIqU9AFSV6Hfs5XbJYcZMllvKZLILnqO3fAoGJ_cLXw9h5PNxPUTrcOLOjxi35JKcf-I0rWDc_uEp4nzDkZswHzCu_DPxlBd7PLVYN44UyWc-Edp0xd6OOCV692dessfPn77ffS0evn25v7t9KIyEJhe6adBAaxFAS93auh2ox7Gvm7rRRFRR21R21LoH05mu7zS2Wg-ybXBsZT_Ul-z91nuI4edCKavZJUPThJ7CkpToRQu1lKJao3KLmhhSijSqQ3QzxqMSoE621V5tttXJtgKhVtsrdvMXZlx-lZcjuul_8McNptXBs6OoknHkDVkXV7PKBvfvgt_cH6RS
CitedBy_id	crossref_primary_10_1007_s10489_019_01461_0 crossref_primary_10_1016_j_procs_2018_05_034 crossref_primary_10_1109_ACCESS_2022_3165966 crossref_primary_10_1109_JSEN_2020_2984493 crossref_primary_10_1016_j_bspc_2019_101700 crossref_primary_10_1016_j_knosys_2019_105446 crossref_primary_10_3389_fcvm_2022_1001982 crossref_primary_10_1016_j_bspc_2023_104671 crossref_primary_10_1109_TIM_2018_2816458 crossref_primary_10_1016_j_bspc_2023_105766 crossref_primary_10_1016_j_cmpb_2018_05_009 crossref_primary_10_1016_j_procs_2022_09_342 crossref_primary_10_1007_s11042_020_08769_x crossref_primary_10_1016_j_bbe_2018_04_004 crossref_primary_10_2478_jce_2023_0019 crossref_primary_10_1016_j_knosys_2024_111906 crossref_primary_10_1016_j_bspc_2023_104701 crossref_primary_10_1016_j_ins_2016_10_013 crossref_primary_10_1016_j_irbm_2019_09_003 crossref_primary_10_3390_bioengineering9090430 crossref_primary_10_1016_j_eswa_2023_122402 crossref_primary_10_1016_j_ejmp_2019_05_004 crossref_primary_10_1016_j_knosys_2017_06_026 crossref_primary_10_1016_j_bspc_2022_104041 crossref_primary_10_3389_fphys_2022_783184 crossref_primary_10_1016_j_jocs_2017_03_005 crossref_primary_10_1016_j_knosys_2018_09_001 crossref_primary_10_3390_bios15060392 crossref_primary_10_1109_JBHI_2021_3100425 crossref_primary_10_1515_bmt_2020_0329 crossref_primary_10_1109_TIM_2023_3258521 crossref_primary_10_1155_2021_4123471 crossref_primary_10_1007_s11760_017_1146_z crossref_primary_10_1109_JSEN_2024_3351918 crossref_primary_10_1109_ACCESS_2021_3095248 crossref_primary_10_1109_JSEN_2019_2935552 crossref_primary_10_1016_j_ins_2017_06_027 crossref_primary_10_3390_pr10112348 crossref_primary_10_1016_j_bbe_2017_12_002 crossref_primary_10_1016_j_artmed_2021_102179 crossref_primary_10_3390_app122412957 crossref_primary_10_1016_j_artmed_2019_101789 crossref_primary_10_1016_j_bbe_2017_08_005 crossref_primary_10_1016_j_cmpb_2021_105941 crossref_primary_10_1016_j_eswa_2025_126901 crossref_primary_10_1109_JSEN_2019_2896308 crossref_primary_10_1088_1361_6579_ad3d25 crossref_primary_10_1155_2021_6455053 crossref_primary_10_1016_j_cmpb_2022_107124 crossref_primary_10_3390_a18020090 crossref_primary_10_1007_s11760_021_02009_x crossref_primary_10_1109_ACCESS_2019_2955555 crossref_primary_10_1109_ACCESS_2020_3026968 crossref_primary_10_1049_htl_2016_0089 crossref_primary_10_1109_TIM_2021_3132833 crossref_primary_10_1007_s13042_022_01718_0 crossref_primary_10_1038_s41467_020_17804_2 crossref_primary_10_3389_fphys_2022_854191 crossref_primary_10_1109_ACCESS_2019_2912519 crossref_primary_10_1016_j_engappai_2020_104092 crossref_primary_10_1088_1361_6579_ad46e1 crossref_primary_10_1097_MCA_0000000000000763 crossref_primary_10_1038_s41598_017_06596_z crossref_primary_10_1142_S0219519417400085 crossref_primary_10_3390_s20174777 crossref_primary_10_1109_TBCAS_2018_2848477 crossref_primary_10_1109_TIM_2021_3104394 crossref_primary_10_1145_3534580 crossref_primary_10_3390_app12115603 crossref_primary_10_3390_s17091937 crossref_primary_10_1016_j_asoc_2025_113655 crossref_primary_10_1515_bmt_2022_0406 crossref_primary_10_3390_s20041020 crossref_primary_10_3390_e19090488 crossref_primary_10_1016_j_asoc_2023_110919 crossref_primary_10_1016_j_bbe_2019_05_010 crossref_primary_10_1016_j_bspc_2020_101997 crossref_primary_10_1016_j_knosys_2019_105402 crossref_primary_10_1016_j_cmpb_2021_106379 crossref_primary_10_1109_TIM_2024_3418105 crossref_primary_10_1016_j_bspc_2022_103663 crossref_primary_10_1016_j_bspc_2024_106377 crossref_primary_10_1117_1_JEI_34_1_013050 crossref_primary_10_1007_s11042_024_18116_z crossref_primary_10_1109_RBME_2017_2757953 crossref_primary_10_1016_j_patrec_2019_02_016 crossref_primary_10_3390_ijerph18115838 crossref_primary_10_1016_j_bspc_2021_102846 crossref_primary_10_1016_j_ins_2021_12_083 crossref_primary_10_1088_1361_6579_ad6529 crossref_primary_10_1109_TMC_2020_3012681 crossref_primary_10_1016_j_asoc_2020_106383 crossref_primary_10_1049_sil2_12072 crossref_primary_10_3390_electronics10020170 crossref_primary_10_1088_1361_6579_ac7fd9 crossref_primary_10_1016_j_cogsys_2019_09_001 crossref_primary_10_1016_j_bspc_2024_106382 crossref_primary_10_3389_fcvm_2022_860032 crossref_primary_10_1007_s10489_021_02696_6 crossref_primary_10_1007_s11760_017_1068_9 crossref_primary_10_1016_j_bspc_2023_105710 crossref_primary_10_3390_s19112558 crossref_primary_10_1109_JBHI_2019_2910082 crossref_primary_10_1016_j_bbe_2020_06_004 crossref_primary_10_1016_j_bspc_2021_102683 crossref_primary_10_1016_j_cmpb_2021_106024 crossref_primary_10_1016_j_knosys_2016_05_027 crossref_primary_10_1007_s12652_020_02536_4 crossref_primary_10_1016_j_bspc_2022_104238 crossref_primary_10_1109_ACCESS_2024_3491073 crossref_primary_10_1049_htl_2020_0015 crossref_primary_10_1155_2021_6630643 crossref_primary_10_3390_s24030828 crossref_primary_10_1007_s12652_023_04745_z crossref_primary_10_1109_ACCESS_2019_2919068 crossref_primary_10_3390_healthcare10020232 crossref_primary_10_1016_j_knosys_2016_07_004 crossref_primary_10_1016_j_cmpb_2019_03_012 crossref_primary_10_1016_j_ins_2023_118978 crossref_primary_10_1016_j_cmpb_2019_105138 crossref_primary_10_1016_j_cmpb_2021_106035 crossref_primary_10_1016_j_ijcard_2021_11_039 crossref_primary_10_1016_j_ins_2022_05_070 crossref_primary_10_1016_j_bspc_2023_104725 crossref_primary_10_1016_j_engappai_2025_111894 crossref_primary_10_1109_JBHI_2022_3145999 crossref_primary_10_1134_S1054661819040151 crossref_primary_10_1109_ACCESS_2024_3401744 crossref_primary_10_1109_TIM_2025_3545189 crossref_primary_10_1016_j_cmpb_2018_04_018 crossref_primary_10_3389_fphys_2019_00809 crossref_primary_10_1109_LSENS_2024_3450176 crossref_primary_10_3390_s20247246 crossref_primary_10_1016_j_premed_2025_100021 crossref_primary_10_1109_JSEN_2024_3523035 crossref_primary_10_1080_10255842_2023_2270101 crossref_primary_10_1109_TIM_2021_3117663 crossref_primary_10_1007_s11042_023_17246_0 crossref_primary_10_1007_s11760_019_01617_y crossref_primary_10_1016_j_cmpb_2019_105120 crossref_primary_10_1109_JSEN_2021_3079241 crossref_primary_10_1088_1361_6579_acaa1a crossref_primary_10_1016_j_knosys_2021_107473 crossref_primary_10_1007_s10916_016_0505_6 crossref_primary_10_3390_s22134960 crossref_primary_10_1016_j_compbiomed_2022_105599 crossref_primary_10_1109_JBHI_2021_3060433 crossref_primary_10_32604_cmc_2022_022123 crossref_primary_10_1016_j_knosys_2019_04_023 crossref_primary_10_1016_j_compbiomed_2018_09_027 crossref_primary_10_4018_IJSKD_313589 crossref_primary_10_1109_ACCESS_2019_2946932 crossref_primary_10_3390_s18082739
Cites_doi	10.1016/S0165-0270(00)00356-3 10.4236/jbise.2014.710081 10.1109/RBME.2012.2184750 10.1007/978-1-4899-2305-9_2 10.1515/zna-1982-1117 10.1088/0967-3334/26/5/R01 10.1111/j.1475-097X.2007.00761.x 10.1136/bmj.301.6758.941 10.1016/j.mcna.2007.03.007 10.1016/S0375-9601(03)00949-6 10.1016/0167-2789(93)90009-P 10.1103/PhysRevLett.88.174102 10.1016/j.compbiomed.2014.08.010 10.3390/e14081553 10.1109/TBME.2012.2213597 10.1136/hrt.71.4.311 10.1378/chest.108.6.1502 10.1103/PhysRevA.36.842 10.1016/j.medengphy.2008.06.010 10.1016/0167-2789(88)90081-4 10.1109/CIC.1992.269345 10.1136/hrt.71.4.309 10.1016/j.knosys.2015.08.004 10.1016/0020-0255(86)90006-X 10.1007/s10916-010-9474-3 10.1002/j.1538-7305.1948.tb01338.x 10.1161/01.CIR.101.23.e215 10.1114/1.1541013 10.1016/j.eplepsyres.2007.08.002 10.1073/pnas.88.6.2297
ContentType	Journal Article
Copyright	2016
Copyright_xml	– notice: 2016
DBID	AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1016/j.knosys.2016.01.040
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	1872-7409
EndPage	156
ExternalDocumentID	10_1016_j_knosys_2016_01_040 S0950705116000708
GroupedDBID	--K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABYKQ ACAZW ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADGUI ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SST SSV SSW SSZ T5K WH7 XPP ZMT ~02 ~G- 29L 77I 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABDPE 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 FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW UHS WUQ ~HD 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c405t-b55ac06da00b4b6d369e8af83535beee2e652dfbb80c7c787ba6bb9465af64893
ISICitedReferencesCount	189
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000374603400014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0950-7051
IngestDate	Mon Sep 29 05:26:42 EDT 2025 Tue Nov 18 22:24:52 EST 2025 Sat Nov 29 06:41:29 EST 2025 Fri Feb 23 02:28:22 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Myocardial infarction Entropy Discrete wavelet transform Classifier Electrocardiogram
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c405t-b55ac06da00b4b6d369e8af83535beee2e652dfbb80c7c787ba6bb9465af64893
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PQID	1816034412
PQPubID	23500
PageCount	11
ParticipantIDs	proquest_miscellaneous_1816034412 crossref_primary_10_1016_j_knosys_2016_01_040 crossref_citationtrail_10_1016_j_knosys_2016_01_040 elsevier_sciencedirect_doi_10_1016_j_knosys_2016_01_040
PublicationCentury	2000
PublicationDate	2016-05-01 2016-05-00 20160501
PublicationDateYYYYMMDD	2016-05-01
PublicationDate_xml	– month: 05 year: 2016 text: 2016-05-01 day: 01
PublicationDecade	2010
PublicationTitle	Knowledge-based systems
PublicationYear	2016
Publisher	Elsevier B.V
Publisher_xml	– name: Elsevier B.V
References	Reddy, Svensson, Haisty, Pahlm (bib0014) 1992 Lu, Ong, Chia (bib0049) 2000; 27 Lahiri, Kumar, Mishra, Sarkar, Roy (bib0050) 2009; 68 Desai, Martis, Nayak, Sarika, Nayak, Shirva, Nayak, Mudassir (bib0046) 2015 Farmer (bib0025) 1982; 37 Zhang, Cowan, Bluemke, Finn, Fonseca, Kadish, Lee, Lima, Suinesiaputra, Young, Medrano-Gracia (bib0005) 2014; 9 Tragardh, Claesson, Wagner, Zhou, Pahlm (bib0012) 2007; 27 Banarjee, Mitra (bib0042) 2012; 1 Sun, Lu, Yang, Li (bib0043) 2012; 59 Sprott (bib0038) 2003 Arif, Malagore, Afsar (bib0041) 2012; 36 Luna, Fiol-Sala, Antman (bib0011) 2006 Li, Ouyang, Richards (bib0047) 2007; 77 Timmis (bib0007) 1994; 71 Pincus (bib0023) 1991; 88 Renyi (bib0028) 1961; 1 Goldberger, Amaral, Glass, Hausdorff, Ivanov (bib0018) 2000; 101 Kaspar, Schuster (bib0037) 1987; 36 Zanin, Zunino, Rosso, Papo (bib0048) 2012; 14 WHO (bib0001) 2011 Tong, Bezerianos, Malhotra, Zhu, Thakor (bib0031) 2003; 314 Higuchi (bib0034) 1988; 31 Roger (bib0002) 2007; 91 Rosso, Blanco, Yordanova, Kolev, Figliola, Schurmann, Basar (bib0032) 2001; 105 Liu, Liu, Wang, Huang, Li, Zheng, Luo, Zhou (bib0045) 2014; 61 Bandt, Pompe (bib0026) 2002; 88 Gomez, Mediavilla, Hornero, Abasolo, Fernandez (bib0035) 2009; 31 Mandelbrot (bib0033) 1982 AHA (bib0003) 2013; 127 Timmis (bib0006) 1990; 301 Duda, Peter, David (bib0040) 2012 Lewis K.M., Handal K. Sensible Analysis of the 12 Lead ECG, 1st edition, 2000. McDarby, Celler, Lovell (bib0015) 1998 Pan, Tompkins (bib0020) 2006 Bozzola, Bortolan, Combi, Pinciroli, Brohet (bib0016) 1996 Kolmogorov (bib0036) 1963; 25 Martis, Acharya, Lim (bib0019) 2012; 8 AHA (bib0004) 2015; 131 Bezerianos, Tong, Thakor (bib0030) 2003; 31 Grassberger (bib0029) 1991; 256 Mair, Smidt, Lechleitner, Dienstl, Puschendorf (bib0009) 1995; 108 Shannon (bib0027) 1948; 27 Addison (bib0021) 2005; 26 Martis, Chakraborty, Ray (bib0017) 2009 Rosenstein, Colins, De Luca (bib0039) 1993; 65 Lee, Cross, Garthwaite, Dickie, Ross, Walton, Jennings (bib0008) 1994; 71 Safdarian, Dabanloo, Attarodi (bib0044) 2014; 7 Acharya, Fujita, Sudarshan, Bhat, Koh (bib0022) 2015; 88 Kosko (bib0024) 1986; 40 Faust, Acharya, Tamura (bib0013) 2012; 5 Bandt (10.1016/j.knosys.2016.01.040_bib0026) 2002; 88 Li (10.1016/j.knosys.2016.01.040_bib0047) 2007; 77 Goldberger (10.1016/j.knosys.2016.01.040_bib0018) 2000; 101 Pincus (10.1016/j.knosys.2016.01.040_bib0023) 1991; 88 Timmis (10.1016/j.knosys.2016.01.040_bib0007) 1994; 71 Arif (10.1016/j.knosys.2016.01.040_bib0041) 2012; 36 McDarby (10.1016/j.knosys.2016.01.040_bib0015) 1998 Gomez (10.1016/j.knosys.2016.01.040_bib0035) 2009; 31 Rosso (10.1016/j.knosys.2016.01.040_bib0032) 2001; 105 10.1016/j.knosys.2016.01.040_bib0010 Pan (10.1016/j.knosys.2016.01.040_bib0020) 2006 Faust (10.1016/j.knosys.2016.01.040_bib0013) 2012; 5 AHA (10.1016/j.knosys.2016.01.040_bib0003) 2013; 127 Higuchi (10.1016/j.knosys.2016.01.040_bib0034) 1988; 31 Tong (10.1016/j.knosys.2016.01.040_bib0031) 2003; 314 Tragardh (10.1016/j.knosys.2016.01.040_bib0012) 2007; 27 Reddy (10.1016/j.knosys.2016.01.040_bib0014) 1992 Safdarian (10.1016/j.knosys.2016.01.040_bib0044) 2014; 7 Liu (10.1016/j.knosys.2016.01.040_bib0045) 2014; 61 Addison (10.1016/j.knosys.2016.01.040_bib0021) 2005; 26 Bozzola (10.1016/j.knosys.2016.01.040_bib0016) 1996 Banarjee (10.1016/j.knosys.2016.01.040_bib0042) 2012; 1 Farmer (10.1016/j.knosys.2016.01.040_bib0025) 1982; 37 Mandelbrot (10.1016/j.knosys.2016.01.040_bib0033) 1982 Kaspar (10.1016/j.knosys.2016.01.040_bib0037) 1987; 36 Bezerianos (10.1016/j.knosys.2016.01.040_bib0030) 2003; 31 Martis (10.1016/j.knosys.2016.01.040_bib0017) 2009 Rosenstein (10.1016/j.knosys.2016.01.040_bib0039) 1993; 65 Renyi (10.1016/j.knosys.2016.01.040_bib0028) 1961; 1 Acharya (10.1016/j.knosys.2016.01.040_bib0022) 2015; 88 Lee (10.1016/j.knosys.2016.01.040_bib0008) 1994; 71 AHA (10.1016/j.knosys.2016.01.040_bib0004) 2015; 131 Desai (10.1016/j.knosys.2016.01.040_bib0046) 2015 Roger (10.1016/j.knosys.2016.01.040_bib0002) 2007; 91 Luna (10.1016/j.knosys.2016.01.040_bib0011) 2006 Zanin (10.1016/j.knosys.2016.01.040_bib0048) 2012; 14 Kolmogorov (10.1016/j.knosys.2016.01.040_bib0036) 1963; 25 Kosko (10.1016/j.knosys.2016.01.040_bib0024) 1986; 40 Lahiri (10.1016/j.knosys.2016.01.040_bib0050) 2009; 68 Martis (10.1016/j.knosys.2016.01.040_bib0019) 2012; 8 WHO (10.1016/j.knosys.2016.01.040_bib0001) Duda (10.1016/j.knosys.2016.01.040_bib0040) 2012 Shannon (10.1016/j.knosys.2016.01.040_bib0027) 1948; 27 Grassberger (10.1016/j.knosys.2016.01.040_bib0029) 1991; 256 Lu (10.1016/j.knosys.2016.01.040_bib0049) 2000; 27 Timmis (10.1016/j.knosys.2016.01.040_bib0006) 1990; 301 Sprott (10.1016/j.knosys.2016.01.040_bib0038) 2003 Mair (10.1016/j.knosys.2016.01.040_bib0009) 1995; 108 Sun (10.1016/j.knosys.2016.01.040_bib0043) 2012; 59 Zhang (10.1016/j.knosys.2016.01.040_bib0005) 2014; 9
References_xml	– year: 1982 ident: bib0033 article-title: The Fractal Geometry of Nature – reference: Lewis K.M., Handal K. Sensible Analysis of the 12 Lead ECG, 1st edition, 2000. – volume: 105 start-page: 65 year: 2001 end-page: 75 ident: bib0032 article-title: Wavelet entropy: a new tool for analysis of short duration electrical signals publication-title: J. Neurosci. Methods – volume: 27 start-page: 387 year: 2000 end-page: 390 ident: bib0049 article-title: An automated ECG classification system based on a neuro-fuzzy system publication-title: Comput. Cardiol. – volume: 108 start-page: 1502 year: 1995 end-page: 1509 ident: bib0009 article-title: A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission publication-title: Chest – volume: 256 start-page: 15 year: 1991 end-page: 33 ident: bib0029 article-title: Information and complexity measures in dynamical systems publication-title: Information Dynamics – start-page: 667 year: 1992 end-page: 670 ident: bib0014 article-title: Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG publication-title: Proc. Comput. Cardiol. – volume: 65 start-page: 117 year: 1993 end-page: 134 ident: bib0039 article-title: A practical method for calculating largest Lyapunov exponent from small data sets publication-title: Physica D – year: 2003 ident: bib0038 article-title: Chaos and Time-Series Analysis – volume: 36 start-page: 279 year: 2012 end-page: 289 ident: bib0041 article-title: Detection and Localization of Myocardial Infarction Using K-nearest Neighbor Classifier publication-title: J. Med. Syst. – year: 2006 ident: bib0011 article-title: The 12 Lead ECG in ST Elevation Myocardial Infarction: A Practical Approach for Clinicians – volume: 77 start-page: 70 year: 2007 ident: bib0047 article-title: Predictability analysis of absence seizures with permutation entropy publication-title: Epilepsy Res – volume: 26 start-page: R155 year: 2005 end-page: R199 ident: bib0021 article-title: Wavelet transforms and the ECG: a review publication-title: Physiol. Meas. – start-page: 31 year: 1998 end-page: 32 ident: bib0015 article-title: Characterizing the discrete wavelet transform of an ECG signal with simple parameters for use in automated diagnosis publication-title: 2nd International Conference on Bioelectromagnetism – year: 2006 ident: bib0020 article-title: A Real Time QRS Detection Algorithm – volume: 31 start-page: 277 year: 1988 end-page: 283 ident: bib0034 article-title: Approach to an irregular time series on the basis of the fractal theory publication-title: Physics D – start-page: 1 year: 2009 end-page: 4 ident: bib0017 article-title: An integrated ECG feature extraction scheme using PCA and wavelet transform publication-title: 2009 India Annual IEEE Conference (INDICON) – volume: 314 start-page: 354 year: 2003 end-page: 361 ident: bib0031 article-title: Parameterized entropy analysis of EEG following hypoxic ischemic brain injury publication-title: Phys. Lett. A – volume: 36 start-page: 842 year: 1987 ident: bib0037 article-title: Easily calculable measure for the complexity of spatiotemporal patterns publication-title: Phys. Rev. – volume: 91 start-page: 537 year: 2007 end-page: 552 ident: bib0002 article-title: Epidemiology of myocardial infarction publication-title: Med. Clin. N. Am. – volume: 71 start-page: 311 year: 1994 end-page: 315 ident: bib0008 article-title: Comparison of the value of novel rapid measurement of myoglobin, creatine kinase, and creatine kinase-MB with the electrocardiogram for the diagnosis of acute myocardial infarction publication-title: Br. Heart J. – volume: 37 start-page: 1304 year: 1982 end-page: 1325 ident: bib0025 article-title: Information dimension and the probabilistic structure of chaos publication-title: Naturforsch. Z. – volume: 131 start-page: e29 year: 2015 end-page: e322 ident: bib0004 article-title: Heart disease and stroke statistics, A report from the American Heart Association publication-title: Circulation – volume: 127 start-page: e6 year: 2013 end-page: e245 ident: bib0003 article-title: Heart disease and stroke statistics, A report from the American Heart Association (AHA) publication-title: Circulation – year: 2012 ident: bib0040 article-title: Pattern Classification – start-page: 241 year: 1996 end-page: 244 ident: bib0016 article-title: A hybrid neuro-fuzzy system for ECG classification of myocardial infarction publication-title: Proc. Comput. Cardiol. – volume: 301 start-page: 941 year: 1990 end-page: 942 ident: bib0006 article-title: Early diagnosis of acute myocardial infarction publication-title: BMJ – volume: 101 start-page: e215 year: 2000 end-page: e220 ident: bib0018 article-title: PhysioBank, PhysioToolKit, and PhysioNet: components of a new research resource for complex physiologic signals publication-title: Circulation – volume: 88 start-page: 85 year: 2015 end-page: 96 ident: bib0022 article-title: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review publication-title: Knowledge Based Syst. – volume: 27 start-page: 379 year: 1948 end-page: 423 ident: bib0027 article-title: A mathematical theory of communication publication-title: Bell Syst. Tech. J. – volume: 1 start-page: 88 year: 2012 end-page: 92 ident: bib0042 article-title: Cross wavelet transform based analysis of electrocardiogram signals publication-title: Int. J. Electr. Electron. Comput. Eng. – volume: 61 start-page: 178 year: 2014 end-page: 184 ident: bib0045 article-title: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection publication-title: Comput. Biol. Med. – volume: 1 start-page: 547 year: 1961 end-page: 561 ident: bib0028 article-title: On measures of entropy and information publication-title: Proc. Fourth Berkeley Symp. On Math. Sttis. And Prob. – volume: 40 start-page: 165 year: 1986 end-page: 174 ident: bib0024 article-title: Fuzzy entropy and conditioning publication-title: Inf. Sci. – volume: 31 start-page: 306 year: 2009 end-page: 313 ident: bib0035 article-title: Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients publication-title: Med. Eng. Phys. – volume: 88 year: 2002 ident: bib0026 article-title: Permutation entropy: a natural complexity measure for time series publication-title: Rev. Lett. – volume: 27 start-page: 368 year: 2007 end-page: 374 ident: bib0012 article-title: Detection of acute myocardial infarction using 12-lead ECG plus inverted leads versus the 16-lead ECG (with additional posterior and right-sided chest electrodes) publication-title: Clin. Physiol. Funct. Imaging – start-page: 153 year: 2015 end-page: 162 ident: bib0046 article-title: Discrete cosine transform features in automated classification of cardiac arrhythmia beats publication-title: Emerging Research in Computing, Information, Communication and Applications – volume: 5 start-page: 15 year: 2012 end-page: 28 ident: bib0013 article-title: Formal design methods for reliable computer-aided diagnosis: a review publication-title: IEEE Rev. Biomed. Eng. – volume: 9 year: 2014 ident: bib0005 article-title: Atlas-based quantification of cardiac remodeling due to myocardial infarction publication-title: PLoS One – volume: 31 start-page: 221 year: 2003 end-page: 232 ident: bib0030 article-title: Time dependent entropy of EEG rhythm changes following brain ischemia publication-title: Ann. Biomed. Eng. – volume: 68 start-page: 866 year: 2009 end-page: 870 ident: bib0050 article-title: Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction publication-title: J. Sci. Ind. Res. – volume: 88 start-page: 2297 year: 1991 end-page: 2301 ident: bib0023 article-title: Approximate entropy as a measure of system complexity publication-title: Proc. Natl. Acad. Sci. – volume: 59 year: 2012 ident: bib0043 article-title: ECG analysis using multiple instance learning for myocardial infarction detection publication-title: IEEE Trans. Biomed. Eng. – year: 2011 ident: bib0001 article-title: Fact Sheet – volume: 71 start-page: 309 year: 1994 end-page: 310 ident: bib0007 article-title: Will serum enzymes and other proteins find a clinical application in the early diagnosis of myocardial infarction? publication-title: Br. Heart J. – volume: 8 start-page: 437 year: 2012 end-page: 448 ident: bib0019 article-title: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform publication-title: Knowledge Based-Syst. – volume: 7 start-page: 818 year: 2014 end-page: 824 ident: bib0044 article-title: A new pattern recognition method for detection and localization of myocardial infarction using t-wave integral and total integral as extracted features from one cycle of ECG signal publication-title: J. Biomed. Sci. Eng. – volume: 14 start-page: 1553 year: 2012 end-page: 1577 ident: bib0048 article-title: Permutation entropy and its main biomedical and econophysics application: a review publication-title: Entropy – volume: 25 start-page: 369 year: 1963 end-page: 376 ident: bib0036 article-title: On tables of random numbers publication-title: Sankhya: Indian J. Statist. Ser. A. (1961–2002) – ident: 10.1016/j.knosys.2016.01.040_bib0001 – volume: 105 start-page: 65 year: 2001 ident: 10.1016/j.knosys.2016.01.040_bib0032 article-title: Wavelet entropy: a new tool for analysis of short duration electrical signals publication-title: J. Neurosci. Methods doi: 10.1016/S0165-0270(00)00356-3 – volume: 7 start-page: 818 year: 2014 ident: 10.1016/j.knosys.2016.01.040_bib0044 article-title: A new pattern recognition method for detection and localization of myocardial infarction using t-wave integral and total integral as extracted features from one cycle of ECG signal publication-title: J. Biomed. Sci. Eng. doi: 10.4236/jbise.2014.710081 – year: 2003 ident: 10.1016/j.knosys.2016.01.040_bib0038 – volume: 5 start-page: 15 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0013 article-title: Formal design methods for reliable computer-aided diagnosis: a review publication-title: IEEE Rev. Biomed. Eng. doi: 10.1109/RBME.2012.2184750 – volume: 256 start-page: 15 year: 1991 ident: 10.1016/j.knosys.2016.01.040_bib0029 article-title: Information and complexity measures in dynamical systems publication-title: Information Dynamics doi: 10.1007/978-1-4899-2305-9_2 – year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0040 – volume: 1 start-page: 547 year: 1961 ident: 10.1016/j.knosys.2016.01.040_bib0028 article-title: On measures of entropy and information – volume: 37 start-page: 1304 year: 1982 ident: 10.1016/j.knosys.2016.01.040_bib0025 article-title: Information dimension and the probabilistic structure of chaos publication-title: Naturforsch. Z. doi: 10.1515/zna-1982-1117 – start-page: 31 year: 1998 ident: 10.1016/j.knosys.2016.01.040_bib0015 article-title: Characterizing the discrete wavelet transform of an ECG signal with simple parameters for use in automated diagnosis – volume: 25 start-page: 369 year: 1963 ident: 10.1016/j.knosys.2016.01.040_bib0036 article-title: On tables of random numbers publication-title: Sankhya: Indian J. Statist. Ser. A. (1961–2002) – volume: 26 start-page: R155 issue: 5 year: 2005 ident: 10.1016/j.knosys.2016.01.040_bib0021 article-title: Wavelet transforms and the ECG: a review publication-title: Physiol. Meas. doi: 10.1088/0967-3334/26/5/R01 – volume: 27 start-page: 368 year: 2007 ident: 10.1016/j.knosys.2016.01.040_bib0012 article-title: Detection of acute myocardial infarction using 12-lead ECG plus inverted leads versus the 16-lead ECG (with additional posterior and right-sided chest electrodes) publication-title: Clin. Physiol. Funct. Imaging doi: 10.1111/j.1475-097X.2007.00761.x – volume: 301 start-page: 941 year: 1990 ident: 10.1016/j.knosys.2016.01.040_bib0006 article-title: Early diagnosis of acute myocardial infarction publication-title: BMJ doi: 10.1136/bmj.301.6758.941 – volume: 91 start-page: 537 year: 2007 ident: 10.1016/j.knosys.2016.01.040_bib0002 article-title: Epidemiology of myocardial infarction publication-title: Med. Clin. N. Am. doi: 10.1016/j.mcna.2007.03.007 – volume: 314 start-page: 354 year: 2003 ident: 10.1016/j.knosys.2016.01.040_bib0031 article-title: Parameterized entropy analysis of EEG following hypoxic ischemic brain injury publication-title: Phys. Lett. A doi: 10.1016/S0375-9601(03)00949-6 – volume: 68 start-page: 866 year: 2009 ident: 10.1016/j.knosys.2016.01.040_bib0050 article-title: Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction publication-title: J. Sci. Ind. Res. – volume: 65 start-page: 117 year: 1993 ident: 10.1016/j.knosys.2016.01.040_bib0039 article-title: A practical method for calculating largest Lyapunov exponent from small data sets publication-title: Physica D doi: 10.1016/0167-2789(93)90009-P – volume: 88 year: 2002 ident: 10.1016/j.knosys.2016.01.040_bib0026 article-title: Permutation entropy: a natural complexity measure for time series publication-title: Rev. Lett. doi: 10.1103/PhysRevLett.88.174102 – volume: 61 start-page: 178 year: 2014 ident: 10.1016/j.knosys.2016.01.040_bib0045 article-title: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2014.08.010 – volume: 14 start-page: 1553 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0048 article-title: Permutation entropy and its main biomedical and econophysics application: a review publication-title: Entropy doi: 10.3390/e14081553 – volume: 59 issue: 12 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0043 article-title: ECG analysis using multiple instance learning for myocardial infarction detection publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2012.2213597 – volume: 9 year: 2014 ident: 10.1016/j.knosys.2016.01.040_bib0005 article-title: Atlas-based quantification of cardiac remodeling due to myocardial infarction publication-title: PLoS One – volume: 71 start-page: 311 year: 1994 ident: 10.1016/j.knosys.2016.01.040_bib0008 article-title: Comparison of the value of novel rapid measurement of myoglobin, creatine kinase, and creatine kinase-MB with the electrocardiogram for the diagnosis of acute myocardial infarction publication-title: Br. Heart J. doi: 10.1136/hrt.71.4.311 – volume: 108 start-page: 1502 year: 1995 ident: 10.1016/j.knosys.2016.01.040_bib0009 article-title: A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission publication-title: Chest doi: 10.1378/chest.108.6.1502 – volume: 36 start-page: 842 year: 1987 ident: 10.1016/j.knosys.2016.01.040_bib0037 article-title: Easily calculable measure for the complexity of spatiotemporal patterns publication-title: Phys. Rev. doi: 10.1103/PhysRevA.36.842 – volume: 31 start-page: 306 year: 2009 ident: 10.1016/j.knosys.2016.01.040_bib0035 article-title: Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2008.06.010 – volume: 1 start-page: 88 issue: 2 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0042 article-title: Cross wavelet transform based analysis of electrocardiogram signals publication-title: Int. J. Electr. Electron. Comput. Eng. – volume: 31 start-page: 277 year: 1988 ident: 10.1016/j.knosys.2016.01.040_bib0034 article-title: Approach to an irregular time series on the basis of the fractal theory publication-title: Physics D doi: 10.1016/0167-2789(88)90081-4 – ident: 10.1016/j.knosys.2016.01.040_bib0010 – volume: 8 start-page: 437 issue: 5 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0019 article-title: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform publication-title: Knowledge Based-Syst. – start-page: 667 year: 1992 ident: 10.1016/j.knosys.2016.01.040_bib0014 article-title: Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG publication-title: Proc. Comput. Cardiol. doi: 10.1109/CIC.1992.269345 – volume: 71 start-page: 309 year: 1994 ident: 10.1016/j.knosys.2016.01.040_bib0007 article-title: Will serum enzymes and other proteins find a clinical application in the early diagnosis of myocardial infarction? publication-title: Br. Heart J. doi: 10.1136/hrt.71.4.309 – volume: 27 start-page: 387 year: 2000 ident: 10.1016/j.knosys.2016.01.040_bib0049 article-title: An automated ECG classification system based on a neuro-fuzzy system publication-title: Comput. Cardiol. – volume: 88 start-page: 85 year: 2015 ident: 10.1016/j.knosys.2016.01.040_bib0022 article-title: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review publication-title: Knowledge Based Syst. doi: 10.1016/j.knosys.2015.08.004 – volume: 40 start-page: 165 year: 1986 ident: 10.1016/j.knosys.2016.01.040_bib0024 article-title: Fuzzy entropy and conditioning publication-title: Inf. Sci. doi: 10.1016/0020-0255(86)90006-X – start-page: 1 year: 2009 ident: 10.1016/j.knosys.2016.01.040_bib0017 article-title: An integrated ECG feature extraction scheme using PCA and wavelet transform – year: 2006 ident: 10.1016/j.knosys.2016.01.040_bib0011 – year: 2006 ident: 10.1016/j.knosys.2016.01.040_bib0020 – start-page: 241 year: 1996 ident: 10.1016/j.knosys.2016.01.040_bib0016 article-title: A hybrid neuro-fuzzy system for ECG classification of myocardial infarction publication-title: Proc. Comput. Cardiol. – volume: 36 start-page: 279 year: 2012 ident: 10.1016/j.knosys.2016.01.040_bib0041 article-title: Detection and Localization of Myocardial Infarction Using K-nearest Neighbor Classifier publication-title: J. Med. Syst. doi: 10.1007/s10916-010-9474-3 – volume: 27 start-page: 379 year: 1948 ident: 10.1016/j.knosys.2016.01.040_bib0027 article-title: A mathematical theory of communication publication-title: Bell Syst. Tech. J. doi: 10.1002/j.1538-7305.1948.tb01338.x – start-page: 153 year: 2015 ident: 10.1016/j.knosys.2016.01.040_bib0046 article-title: Discrete cosine transform features in automated classification of cardiac arrhythmia beats – volume: 101 start-page: e215 year: 2000 ident: 10.1016/j.knosys.2016.01.040_bib0018 article-title: PhysioBank, PhysioToolKit, and PhysioNet: components of a new research resource for complex physiologic signals publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – volume: 127 start-page: e6 year: 2013 ident: 10.1016/j.knosys.2016.01.040_bib0003 article-title: Heart disease and stroke statistics, A report from the American Heart Association (AHA) publication-title: Circulation – volume: 131 start-page: e29 year: 2015 ident: 10.1016/j.knosys.2016.01.040_bib0004 article-title: Heart disease and stroke statistics, A report from the American Heart Association publication-title: Circulation – volume: 31 start-page: 221 year: 2003 ident: 10.1016/j.knosys.2016.01.040_bib0030 article-title: Time dependent entropy of EEG rhythm changes following brain ischemia publication-title: Ann. Biomed. Eng. doi: 10.1114/1.1541013 – year: 1982 ident: 10.1016/j.knosys.2016.01.040_bib0033 – volume: 77 start-page: 70 year: 2007 ident: 10.1016/j.knosys.2016.01.040_bib0047 article-title: Predictability analysis of absence seizures with permutation entropy publication-title: Epilepsy Res doi: 10.1016/j.eplepsyres.2007.08.002 – volume: 88 start-page: 2297 year: 1991 ident: 10.1016/j.knosys.2016.01.040_bib0023 article-title: Approximate entropy as a measure of system complexity publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.88.6.2297
SSID	ssj0002218
Score	2.5538619
Snippet	Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	146
SubjectTerms	Automation Classification Classifier Computer programs Discrete wavelet transform Electrocardiogram Entropy Myocardial infarction Nonlinearity Position (location) Software
Title	Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
URI	https://dx.doi.org/10.1016/j.knosys.2016.01.040 https://www.proquest.com/docview/1816034412
Volume	99
WOSCitedRecordID	wos000374603400014&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: 1872-7409 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9swFBYh3cNedh_rbmiwt-AgO7Zs7y2Mlm4dZaztyJuRLJk46-yS2KV52C_bn9vRzTYNo9tgLybItmx8vuh8OtL5DkJvk4gBbxPc81lMPBjwpMeIYF4giZBxwWE8DHWxifjkJFks0s-j0U-XC3N1EVdVcn2dXv5XU0MbGFulzv6FubtOoQF-g9HhCGaH4x8Zft42NdBQIJJCNtJWAq_ERHstm3WpV9W30LDWeSPwWPgI-kSrYwe2OI4-r_dvmZzofCAVvnFq1K7ESqMqUJi0Ycd2j13AzlPOUljZ6I7Fz1XK11az1_Pp5AtbyUqsOzdx2K5KQ22P2PdS1P3ylYDJ-NIEbr-WYssmx9MuVqyjRKfLdvKpXA4jGj7t9w-aMNtOqo2NVxIvJladVprROolhehCSdDicm3pLdjx28U3j2n2jYb7jNUwAYzX9VtXwIdR-P6q1XI2Q1A097lP1JupFfKoIlko03wviKE3GaG_-4WDxsSMCQaDDy92bu8xNvb1w91m_Y0Y3OIImPmcP0D07Y8Fzg7SHaCSrR-i-qwaCrXN4jH50wMMd8DAADw-Bh-sC98DDPfCwBh7eAd47zPAAdljDTvXSwQ5r2D1B54cHZ--PPFvcw8thjtB4PIpYTqhghPCQUzGjqUxYAROCWcSllIGkUSAKzhOSxzm4Fc4o52lII1ZQpZj0FI2rupLPEA5EDJOOqGAhJ6GIklSp8okgz0WayCAs9tHMfdgst8r3qgDLRea2OK4yY45MmSMjfgbm2Eded9elUX655frY2Syz7NWw0gxgdsudb5yJMxjc1Yodq2TdbjKg31RpcvrB83_u_QW62__JXqJxs27lK3Qnv2rKzfq1xewvhmnUQA
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=Automated+detection+and+localization+of+myocardial+infarction+using+electrocardiogram%3A+a+comparative+study+of+different+leads&rft.jtitle=Knowledge-based+systems&rft.au=Acharya%2C+U.+Rajendra&rft.au=Fujita%2C+Hamido&rft.au=Sudarshan%2C+Vidya+K.&rft.au=Oh%2C+Shu+Lih&rft.date=2016-05-01&rft.pub=Elsevier+B.V&rft.issn=0950-7051&rft.eissn=1872-7409&rft.volume=99&rft.spage=146&rft.epage=156&rft_id=info:doi/10.1016%2Fj.knosys.2016.01.040&rft.externalDocID=S0950705116000708
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon