Journal ArticleDOI
Third-order tensor based analysis of multilead ECG for classification of myocardial infarction
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TLDR
A novel method for detection and localization of myocardial infarction (MI) from the reduced MECG tensor, employing the mode-n singular values (MSVs) and the normalized multiscale wavelet energy (NMWE) of each subband tensor to be accurate in detecting and localizing MI.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2017-01-01. It has received 84 citations till now.read more
Citations
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Journal ArticleDOI
A survey on ECG analysis
Selcan Kaplan Berkaya,Alper Kursat Uysal,Efnan Sora Gunal,Semih Ergin,Serkan Gunal,M. Bilginer Gülmezoğlu +5 more
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
Journal ArticleDOI
Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Ulas Baran Baloglu,Muhammed Talo,Ozal Yildirim,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: A deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.
Journal ArticleDOI
ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.
Chuang Han,Li Shi,Li Shi +2 more
TL;DR: A novel method to detect and locate MI combining a multi-lead residual neural network (ML-ResNet) structure with three residual blocks and feature fusion via 12 leads ECG records which reflects spatial location information of different leads subtly is presented.
Journal ArticleDOI
A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes
TL;DR: The experimental results demonstrate that the combination of FBSE-EWT-based entropy features and DL-LSSVM has the mean accuracy, the mean sensitivity, and the mean specificity values of 99.74%, 99.87%, and 99.60%, respectively, for the detection of MI.
Journal ArticleDOI
Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data
TL;DR: The proposed harmonic phase distribution pattern of the ECG data for MI identification provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
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Tensor Decompositions and Applications
Tamara G. Kolda,Brett W. Bader +1 more
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.