[1]
|
Mathematical Modeling and Supercomputer Technologies
Communications in Computer and Information Science,
2024
DOI:10.1007/978-3-031-52470-7_17
|
|
|
[2]
|
Efficient detection of cardiac abnormalities via a simplified score-based analysis of the ECG signal
Journal of Ambient Intelligence and Humanized Computing,
2024
DOI:10.1007/s12652-023-04745-z
|
|
|
[3]
|
Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments
PLOS ONE,
2024
DOI:10.1371/journal.pone.0291084
|
|
|
[4]
|
Lifting of the 9/7 Wavelet Filter Bank for ECG Signal Analysis Implementation Based on FPGA Target
2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET),
2023
DOI:10.1109/IC_ASET58101.2023.10150801
|
|
|
[5]
|
Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer
Tsinghua Science and Technology,
2023
DOI:10.26599/TST.2022.9010008
|
|
|
[6]
|
Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care
Frontiers in Cardiovascular Medicine,
2022
DOI:10.3389/fcvm.2022.1001982
|
|
|
[7]
|
Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction
Processes,
2022
DOI:10.3390/pr10112348
|
|
|
[8]
|
Early detection of myocardial ischemia in 12‐lead ECG using deterministic learning and ensemble learning
Computer Methods and Programs in Biomedicine,
2022
DOI:10.1016/j.cmpb.2022.107124
|
|
|
[9]
|
Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals
IEEE Access,
2022
DOI:10.1109/ACCESS.2022.3220670
|
|
|
[10]
|
Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care
Frontiers in Cardiovascular Medicine,
2022
DOI:10.3389/fcvm.2022.1001982
|
|
|
[11]
|
SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis
Information Sciences,
2022
DOI:10.1016/j.ins.2021.12.083
|
|
|
[12]
|
An Efficient Method for Detection and Localization of Myocardial Infarction
IEEE Transactions on Instrumentation and Measurement,
2022
DOI:10.1109/TIM.2021.3132833
|
|
|
[13]
|
Automated Localization of Myocardial Infarction of Image-Based Multilead ECG Tensor With Tucker2 Decomposition
IEEE Transactions on Instrumentation and Measurement,
2022
DOI:10.1109/TIM.2021.3104394
|
|
|
[14]
|
Arrhythmia Classification Using Alexnet Model Based on Orthogonal Leads and Different Time Segments
2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus),
2022
DOI:10.1109/ElConRus54750.2022.9755708
|
|
|
[15]
|
Classification of Complete Myocardial Infarction Using Rule-Based Rough Set Method and Rough Set Explorer System
IETE Journal of Research,
2022
DOI:10.1080/03772063.2019.1588175
|
|
|
[16]
|
Application of artificial intelligence techniques for automated detection of myocardial infarction: a review
Physiological Measurement,
2022
DOI:10.1088/1361-6579/ac7fd9
|
|
|
[17]
|
ECG Heartbeat Classification of Myocardial Infarction and Arrhythmia using CNN
2022 IEEE India Council International Subsections Conference (INDISCON),
2022
DOI:10.1109/INDISCON54605.2022.9862885
|
|
|
[18]
|
Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems
Biomedicines,
2022
DOI:10.3390/biomedicines10082013
|
|
|
[19]
|
End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing
Bioengineering,
2022
DOI:10.3390/bioengineering9090430
|
|
|
[20]
|
SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis
Information Sciences,
2022
DOI:10.1016/j.ins.2021.12.083
|
|
|
[21]
|
Research Anthology on Artificial Neural Network Applications
2022
DOI:10.4018/978-1-6684-2408-7.ch073
|
|
|
[22]
|
Early detection of myocardial ischemia in 12‐lead ECG using deterministic learning and ensemble learning
Computer Methods and Programs in Biomedicine,
2022
DOI:10.1016/j.cmpb.2022.107124
|
|
|
[23]
|
QT-STNet: A Spatial and Temporal Network Combined with QT Segment for MI Detection and Location
2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC),
2022
DOI:10.1109/CyberC55534.2022.00038
|
|
|
[24]
|
Automated Localization of Myocardial Infarction of Image-Based Multilead ECG Tensor With Tucker2 Decomposition
IEEE Transactions on Instrumentation and Measurement,
2022
DOI:10.1109/TIM.2021.3104394
|
|
|
[25]
|
Efficient detection of myocardial infarction from single lead ECG signal
Biomedical Signal Processing and Control,
2021
DOI:10.1016/j.bspc.2021.102678
|
|
|
[26]
|
Encyclopedia of Information Science and Technology, Fifth Edition
Advances in Information Quality and Management,
2021
DOI:10.4018/978-1-7998-3479-3.ch132
|
|
|
[27]
|
Temporal Feature-Based Classification Into Myocardial Infarction and Other CVDs Merging CNN and Bi-LSTM From ECG Signal
IEEE Sensors Journal,
2021
DOI:10.1109/JSEN.2021.3079241
|
|
|
[28]
|
Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features
Journal of Healthcare Engineering,
2021
DOI:10.1155/2021/4123471
|
|
|
[29]
|
EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms
Biosensors,
2021
DOI:10.3390/bios12010015
|
|
|
[30]
|
Efficient detection of myocardial infarction from single lead ECG signal
Biomedical Signal Processing and Control,
2021
DOI:10.1016/j.bspc.2021.102678
|
|
|
[31]
|
Convolutional Neural Networks Based Diagnosis of Myocardial Infarction in Electrocardiograms
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS),
2021
DOI:10.1109/ICCCIS51004.2021.9397193
|
|
|
[32]
|
8th European Medical and Biological Engineering Conference
IFMBE Proceedings,
2021
DOI:10.1007/978-3-030-64610-3_40
|
|
|
[33]
|
A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction
Engineering Applications of Artificial Intelligence,
2021
DOI:10.1016/j.engappai.2020.104092
|
|
|
[34]
|
An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier
Sensors,
2021
DOI:10.3390/s21072311
|
|
|
[35]
|
Localization of myocardial infarction with multi-lead ECG based on DenseNet
Computer Methods and Programs in Biomedicine,
2021
DOI:10.1016/j.cmpb.2021.106024
|
|
|
[36]
|
Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
Scientific Reports,
2021
DOI:10.1038/s41598-021-94363-6
|
|
|
[37]
|
Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm
Journal of Medical Signals & Sensors,
2021
DOI:10.4103/jmss.JMSS_24_20
|
|
|
[38]
|
Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
Computers in Biology and Medicine,
2021
DOI:10.1016/j.compbiomed.2021.104457
|
|
|
[39]
|
Localization of myocardial infarction with multi-lead ECG based on DenseNet
Computer Methods and Programs in Biomedicine,
2021
DOI:10.1016/j.cmpb.2021.106024
|
|
|
[40]
|
Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
Computers in Biology and Medicine,
2021
DOI:10.1016/j.compbiomed.2021.104457
|
|
|
[41]
|
Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
Scientific Reports,
2021
DOI:10.1038/s41598-021-94363-6
|
|
|
[42]
|
Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
Nature Communications,
2020
DOI:10.1038/s41467-020-17804-2
|
|
|
[43]
|
Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
Nature Communications,
2020
DOI:10.1038/s41467-020-17804-2
|
|
|
[44]
|
A Novel Approach to Classify Electrocardiogram Signals Using Deep Neural Networks
2020 2nd International Conference on Computer and Information Sciences (ICCIS),
2020
DOI:10.1109/ICCIS49240.2020.9257700
|
|
|
[45]
|
Detection of Myocardial Infarction from ECG Signal Through Combining CNN and Bi-LSTM
2020 11th International Conference on Electrical and Computer Engineering (ICECE),
2020
DOI:10.1109/ICECE51571.2020.9393090
|
|
|
[46]
|
Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey
IEEE Access,
2020
DOI:10.1109/ACCESS.2020.3026968
|
|
|
[47]
|
Myocardial Infarction Localization and Blocked Coronary Artery Identification Using a Deep Learning Method
2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan),
2020
DOI:10.1109/PHM-Jinan48558.2020.00100
|
|
|
[48]
|
ECG Heartbeat Classification Using Ensemble of Efficient Machine Learning Approaches on Imbalanced Datasets
2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT),
2020
DOI:10.1109/ICAICT51780.2020.9333534
|
|
|
[49]
|
ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG
Computer Methods and Programs in Biomedicine,
2020
DOI:10.1016/j.cmpb.2019.105138
|
|
|
[50]
|
Comprehensive electrocardiographic diagnosis based on deep learning
Artificial Intelligence in Medicine,
2020
DOI:10.1016/j.artmed.2019.101789
|
|
|
[51]
|
Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers
Soft Computing,
2020
DOI:10.1007/s00500-020-04700-6
|
|
|
[52]
|
MFB-CBRNN: A Hybrid Network for MI Detection Using 12-Lead ECGs
IEEE Journal of Biomedical and Health Informatics,
2020
DOI:10.1109/JBHI.2019.2910082
|
|
|
[53]
|
GA based KELM Optimization for ECG Classification
Procedia Computer Science,
2020
DOI:10.1016/j.procs.2020.03.322
|
|
|
[54]
|
Heterogeneous Recurrence Analysis of Disease-Altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals
IEEE Journal of Biomedical and Health Informatics,
2020
DOI:10.1109/JBHI.2019.2952285
|
|
|
[55]
|
Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis
Biocybernetics and Biomedical Engineering,
2020
DOI:10.1016/j.bbe.2020.06.004
|
|
|
[56]
|
Localization of Myocardial Infarction from 12 Lead ECG Empowered with Novel Machine Learning
Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control,
2019
DOI:10.1145/3386164.3389084
|
|
|
[57]
|
An Efficient Signal Processing Technique for Automated Myocardial Infarction Detection
2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS),
2019
DOI:10.1109/iSES47678.2019.00031
|
|
|
[58]
|
Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach
IRBM,
2019
DOI:10.1016/j.irbm.2019.09.003
|
|
|
[59]
|
Automatic Classification of ECG Signals in WBAN Based on Convolutional Neural Network and Long-Short Term Memory Network
2019 4th International Conference on Computational Intelligence and Applications (ICCIA),
2019
DOI:10.1109/ICCIA.2019.00027
|
|
|
[60]
|
Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries
Pattern Recognition and Image Analysis,
2019
DOI:10.1134/S1054661819040151
|
|
|
[61]
|
Automated Detection of Myocardial Infarction Using a Gramian Angular Field and Principal Component Analysis Network
IEEE Access,
2019
DOI:10.1109/ACCESS.2019.2955555
|
|
|
[62]
|
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Physiological Measurement,
2019
DOI:10.1088/1361-6579/aaf34d
|
|
|
[63]
|
Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features
Computer Methods and Programs in Biomedicine,
2019
DOI:10.1016/j.cmpb.2019.03.012
|
|
|
[64]
|
Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing
Applied Soft Computing,
2019
DOI:10.1016/j.asoc.2019.04.007
|
|
|
[65]
|
Detection and localization of myocardial infarction based on a convolutional autoencoder
Knowledge-Based Systems,
2019
DOI:10.1016/j.knosys.2019.04.023
|
|
|
[66]
|
Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network
Applied Sciences,
2019
DOI:10.3390/app9091879
|
|
|
[67]
|
Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger
IEEE Access,
2019
DOI:10.1109/ACCESS.2019.2919068
|
|
|
[68]
|
Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments
Biocybernetics and Biomedical Engineering,
2019
DOI:10.1016/j.bbe.2019.05.010
|
|
|
[69]
|
Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records
Applied Sciences,
2019
DOI:10.3390/app9163328
|
|
|
[70]
|
Effects of Inferior Myocardial Infarction Sizes and Sites on Simulated Electrocardiograms Based on a Torso-Heart Model
IEEE Access,
2019
DOI:10.1109/ACCESS.2019.2904707
|
|
|
[71]
|
Localization of Myocardial Infarction from 12 Lead ECG Empowered with Novel Machine Learning
Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control,
2019
DOI:10.1145/3386164.3389084
|
|
|
[72]
|
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
Computer Methods and Programs in Biomedicine,
2018
DOI:10.1016/j.cmpb.2018.04.018
|
|
|
[73]
|
Automated detection of cardiac arrhythmia using deep learning techniques
Procedia Computer Science,
2018
DOI:10.1016/j.procs.2018.05.034
|
|
|
[74]
|
A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank
Computers in Biology and Medicine,
2018
DOI:10.1016/j.compbiomed.2018.07.005
|
|
|
[75]
|
Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data
IEEE Transactions on Instrumentation and Measurement,
2018
DOI:10.1109/TIM.2018.2816458
|
|
|
[76]
|
Analysis of 12-lead electrocardiogram signal based on deep learning
International Journal of Heart Rhythm,
2018
DOI:10.4103/IJHR.IJHR_4_18
|
|
|
[77]
|
Automated detection of cardiac arrhythmia using deep learning techniques
Procedia Computer Science,
2018
DOI:10.1016/j.procs.2018.05.034
|
|
|
[78]
|
VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016
IFMBE Proceedings,
2017
DOI:10.1007/978-981-10-4086-3_125
|
|
|
[79]
|
Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
Information Sciences,
2017
DOI:10.1016/j.ins.2016.10.013
|
|
|
[80]
|
Detection of myocardial infarction from vectorcardiogram using relevance vector machine
Signal, Image and Video Processing,
2017
DOI:10.1007/s11760-017-1068-9
|
|
|
[81]
|
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
Information Sciences,
2017
DOI:10.1016/j.ins.2017.06.027
|
|
|
[82]
|
Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal
Knowledge-Based Systems,
2017
DOI:10.1016/j.knosys.2017.06.026
|
|
|
[83]
|
Advances in Artificial Intelligence: From Theory to Practice
Lecture Notes in Computer Science,
2017
DOI:10.1007/978-3-319-60042-0_30
|
|
|
[84]
|
Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach
Signal, Image and Video Processing,
2017
DOI:10.1007/s11760-017-1146-z
|
|
|
[85]
|
ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm
Computer Methods and Programs in Biomedicine,
2017
DOI:10.1016/j.cmpb.2017.09.015
|
|
|
[86]
|
Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
Entropy,
2017
DOI:10.3390/e19090488
|
|
|
[87]
|
Automated ECG analysis usingFourier harmonic phase
2017 IEEE Region 10 Symposium (TENSYMP),
2017
DOI:10.1109/TENCONSpring.2017.8070022
|
|
|
[88]
|
NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS
Journal of Mechanics in Medicine and Biology,
2017
DOI:10.1142/S0219519417400061
|
|
|
[89]
|
A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records
IEEE Reviews in Biomedical Engineering,
2017
DOI:10.1109/RBME.2017.2757953
|
|
|
[90]
|
Identification of Patients with Myocardial Infarction
Methods of Information in Medicine,
2016
DOI:10.3414/ME15-01-0101
|
|
|
[91]
|
Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
Knowledge-Based Systems,
2016
DOI:10.1016/j.knosys.2016.01.040
|
|
|
[92]
|
Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features
Journal of Medical Systems,
2016
DOI:10.1007/s10916-016-0505-6
|
|
|