Journal ArticleDOI
ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform
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TLDR
Five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed and dimensionality reduced features were fed to the Support Vector Machine, neural network and probabilistic neural network (PNN) classifiers for automated diagnosis.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2013-09-01. It has received 586 citations till now. The article focuses on the topics: Probabilistic neural network & Linear discriminant analysis.read more
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Journal ArticleDOI
A deep convolutional neural network model to classify heartbeats
U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam,Arkadiusz Gertych,Ru San Tan +6 more
TL;DR: A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.
Journal ArticleDOI
Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
Journal ArticleDOI
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
TL;DR: It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks and is an important approach that can be applied to similar signal processing problems.
Journal ArticleDOI
Deep learning approach for active classification of electrocardiogram signals
TL;DR: A novel approach based on deep learning for active classification of electrocardiogram (ECG) signals by learning a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint.
Journal ArticleDOI
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
TL;DR: An automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branches block (RBBB) and atrial premature beats (APB), and premature ventricular contraction (PVC) on ECG signals.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.