Journal of Biomedical Science and Engineering

Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org
"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"
written by Naser Safdarian, Nader Jafarnia Dabanloo, Gholamreza Attarodi,
published by Journal of Biomedical Science and Engineering, Vol.7 No.10, 2014
has been cited by the following article(s):
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[4] Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer
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[5] Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction
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[7] ECG Heartbeat Classification of Myocardial Infarction and Arrhythmia using CNN
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[9] Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems
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[10] Arrhythmia Classification Using Alexnet Model Based on Orthogonal Leads and Different Time Segments
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[11] Abnormality Detection in ECG Signal applying Poincare and Entropy-based Approaches
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[13] Early detection of myocardial ischemia in 12‐lead ECG using deterministic learning and ensemble learning
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[14] SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead …
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[15] Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
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[17] Localization of myocardial infarction with multi-lead ECG based on DenseNet
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[18] Convolutional neural networks based diagnosis of myocardial infarction in electrocardiograms
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[19] A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction
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[20] An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis …
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[21] Temporal Feature-Based Classification into Myocardial Infarction and other CVDs Merging CNN and Bi-LSTM from ECG signal
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[22] Evaluating morphological features of electrocardiogram signals for diagnosing of myocardial infarction using classification-based feature selection
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[23] A Survey of Applications of Artificial Intelligence for Myocardial Infarction Disease Diagnosis
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[24] Efficient detection of myocardial infarction from single lead ECG signal
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[25] 基于形态特征提取的急性下壁心肌梗死 BiLSTM 网络辅助诊断算法
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[26] Artificial Neural Networks in Medicine: Recent Advances
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[27] Application of Machine Learning to Analyse Biomedical Signals for Medical Diagnosis
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[28] Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features
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[29] EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms
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[30] Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm
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[31] Automated Localization of Myocardial Infarction of Image-Based Multilead ECG Tensor With Tucker2 Decomposition
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[32] An Efficient Method for Detection and Localization of Myocardial Infarction
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[34] Acute inferior myocardial infarction detection algorithm based on bilstm network of morphological feature extraction
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[36] Multimodal biometric systems based on different fusion levels of ECG and fingerprint using different classifiers
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[37] Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities
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[38] Comprehensive electrocardiographic diagnosis based on deep learning
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[39] Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis
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[41] Stages-Based ECG Signal Analysis from Traditional Signal Processing to Machine Learning Approaches: A Survey
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[42] CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification
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[43] Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network.
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[44] Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms
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[45] Ecg heartbeat classification using ensemble of efficient machine learning approaches on imbalanced datasets
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[46] Detection of Myocardial Infarction from ECG Signal Through Combining CNN and Bi-LSTM
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[47] Myocardial Infarction Localization and Blocked Coronary Artery Identification Using a Deep Learning Method
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[48] Automated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis
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[49] A Novel Approach to Classify Electrocardiogram Signals Using Deep Neural Networks
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[50] Detection and localization of myocardial infarction based on a convolutional autoencoder
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[51] Localization of Myocardial Infarction from 12 Lead ECG Empowered with Novel Machine Learning
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[52] An Efficient Signal Processing Technique for Automated Myocardial Infarction Detection
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[53] Classification of complete myocardial infarction using rule-based rough set method and rough set explorer system
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[54] Automated Detection and Localization of Myocardial Infarction with Staked Sparse Autoencoder and TreeBagger
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[55] ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG
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[56] Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network
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[57] Automated ECG Analysis for Localizing Thrombus in Culprit Artery using Rule Based Information Fuzzy Network
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[58] Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments
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[59] Application of Heartbeat-Attention Mechanism for Detection of Myocardial Infarction Using 12-Lead ECG Records
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[60] Effects of Inferior Myocardial Infarction Sizes and Sites on Simulated Electrocardiograms Based on a Torso-Heart Model
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[61] Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing
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[62] Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach
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[63] Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features
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[64] MFB-CBRNN: a hybrid network for MI detection using 12-lead ECGs
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[65] A Novel Deep Transfer Learning Method for Detection of Myocardial Infarction
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[66] Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries
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[67] A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database
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[68] Automated Identification of Myocardial Infraction Using Harmonic Phase Distribution Pattern of ECG Data
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[69] Automatic Classification of ECG Signals in WBAN Based on Convolutional Neural Network and Long-Short Term Memory Network
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[70] Automated Detection of Myocardial Infarction Using a Gramian Angular Field and Principal Component Analysis Network
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[71] Heterogeneous Recurrence Analysis of Disease-altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals
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[72] Método para processamento e análise de sinais de eletrocardiogramas através de premissas geométricas
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[73] Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
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[74] Detecting and interpreting myocardial infarctions using fully convolutional neural networks
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[75] Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data
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[76] Automated detection of cardiac arrhythmia using deep learning techniques
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[78] A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank
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[79] ST Segment Analysis for Early Detection of Myocardial Infarction
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[80] Detecting and interpreting myocardial infarction using fully convolutional neural networks
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[81] Analysis of 12-lead electrocardiogram signal based on deep learning
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[82] Diagnosis of Coronary Artery Disease via a Novel Fuzzy Expert System Optimized by Cuckoo Search
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[83] A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records
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[84] Detection of myocardial infarction from vectorcardiogram using relevance vector machine
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[85] Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
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[86] Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of …
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[87] Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal
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[88] Application of Deep Convolutional Neural Network for Automated Detection of Myocardial Infarction Using ECG Signals
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[89] VCG and ECG indexes for classification of patients with Myocardial Infarction
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[90] Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of …
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[91] Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach
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[92] ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm
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[93] A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction using Electrocardiogram and Electronic Health Record
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[94] Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
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[95] Nonlinear analysis of coronary artery disease, myocardial infarction, and normal ECG signals
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[96] Automated ECG analysis usingFourier harmonic phase
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[97] Identification of Patients with Myocardial Infarction
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[98] Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
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[99] Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM
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[100] Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features
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