Classification of ECG Signals Using Extreme Learning Machine
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TLDR
A thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats.Abstract:
An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, they are the k-nearest neighbor classifier (kNN) and the radial basis function neural network classifier (RBF), with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared to traditional classifiers.read more
Citations
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Journal ArticleDOI
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Sushravya Raghunath,Alvaro E. Ulloa Cerna,Linyuan Jing,David P. vanMaanen,Joshua V. Stough,Dustin N. Hartzel,Joseph B. Leader,H. Lester Kirchner,Martin C. Stumpe,Ashraf T. Hafez,Arun Nemani,Tanner Carbonati,Kipp W. Johnson,Katelyn Young,Christopher W. Good,John M. Pfeifer,Aalpen A. Patel,Brian P. Delisle,Amro Alsaid,Dominik Beer,Christopher M. Haggerty,Brandon K. Fornwalt +21 more
TL;DR: Results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians, and identifies at-risk individuals with seemingly normal electrocardiograms.
Journal ArticleDOI
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data
Sushravya Raghunath,Alvaro E. Ulloa Cerna,Linyuan Jing,David P. vanMaanen,Joshua V. Stough,Dustin N. Hartzel,Joseph B. Leader,H. Lester Kirchner,Christopher W. Good,Aalpen A. Patel,Brian P. Delisle,Amro Alsaid,Dominik Beer,Christopher M. Haggerty,Brandon K. Fornwalt +14 more
TL;DR: In this paper, a deep neural network was used to predict one-year mortality from 12-lead ECG voltage-time traces, with an average AUC of 0.85 and Cox proportional hazard ratio of 6.6 (p<0.005) for the two predicted groups (dead vs alive one year after ECG).
Proceedings ArticleDOI
A restricted Boltzmann machine based two-lead electrocardiography classification
TL;DR: The proposed restricted Boltzmann machine learning algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECGs classification or detection problems.
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Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview
TL;DR: The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique.
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ECG assessment based on neural networks with pretraining
TL;DR: A new automatic screening method to assess whether a patient from ambulatory care or emergency should be referred to a cardiology service and is based on deep neural networks with pretraining, which automatically obtain a representation of the input data without resorting to any annotation and simplify the process of assessing normality of ECG signals.
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