Proceedings ArticleDOI
Neural networks for ECG classification
Giovanni Bortolan,R. Degani,J.L. Willems +2 more
- pp 269-272
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TLDR
The performance of the neural network approach in the diagnostic classification of 12-lead electrocardiograms (ECG) is investigated and shows a comparable behavior with the two statistical methods.Abstract:
The performance of the neural network approach in the diagnostic classification of 12-lead electrocardiograms (ECG) is investigated. For this study a validated ECG database established at the University of Leuven is used. Previous results obtained from the same database to derive two classifiers based on statistical models (linear discriminant analysis and logistic discriminant analysis) are taken as reference points in the evaluation. A simple neural network architecture is chosen: the feed-forward structure with the use of the back-propagation algorithm. Sensitivity, specificity, total and partial accuracy are the indices used for the assessment of the performance. The results show a comparable behavior with the two statistical methods. >read more
Citations
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ECG signal analysis through hidden Markov models
TL;DR: This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms, and the results obtained validate the approach for real world application.
Journal ArticleDOI
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances
TL;DR: The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
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A model-based Bayesian framework for ECG beat segmentation
TL;DR: Simulation results show that the Bayesian filtering framework may be effectively used for ECG beat segmentation and extraction of fiducial points and can contribute to and enhance the clinical ECGBeat segmentation performance.
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Artificial neural networks for the diagnosis of atrial fibrillation.
TL;DR: The results show that the use of an artificial neural network can improve the sensitivity of reporting AF from 88.5% using the deterministic approach to 92%, without sacrificing specificity (92.3%).
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Artificial Neural Networks for Recognition of Electrocardiographic Lead Reversal
TL;DR: In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm lead reversals being detected by networks but not by conventional interpretation programs.
References
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Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Book
Adaptive pattern recognition and neural networks
TL;DR: This is a book that will show you even new to old thing, and when you are really dying of adaptive pattern recognition and neural networks, just pick this book; it will be right for you.
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Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets
TL;DR: A neural network learning procedure has been applied to the classification of sonar returns from two undersea targets, a metal cylinder and a similarly shaped rock, and network performance and classification strategy was comparable to that of trained human listeners.