Journal ArticleDOI
An efficient compression of ECG signals using deep convolutional autoencoders
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TLDR
A new deep convolutional autoencoder (CAE) model for compressing ECG signals that can learn to use different ECG records automatically and allow secure data transfer in a low-dimensional form to remote medical centers is proposed.About:
This article is published in Cognitive Systems Research.The article was published on 2018-12-01. It has received 156 citations till now. The article focuses on the topics: Data compression ratio & Autoencoder.read more
Citations
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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
Application of deep transfer learning for automated brain abnormality classification using MR images
Muhammed Talo,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: This study proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images, and achieved 5-fold classification accuracy of 100% on 613 MR images.
Journal ArticleDOI
A new approach for arrhythmia classification using deep coded features and LSTM networks.
TL;DR: A novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
Journal ArticleDOI
A review of deep learning with special emphasis on architectures, applications and recent trends
Saptarshi Sengupta,Sanchita Basak,Pallabi Saikia,Sayak Paul,Vasilios Tsalavoutis,Frederick Ditliac Atiah,Vadlamani Ravi,Alan Peters +7 more
TL;DR: The thrust of this review is to outline emerging applications of DL and provide a reference to researchers seeking to use DL in their work for pattern recognition with unparalleled learning capacity and the ability to scale with data.
Journal ArticleDOI
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
TL;DR: A DAE using the fully convolutional network (FCN) is proposed for ECG signal denoising and it is believed that the proposed FCN-based DAE has a good application prospect in clinical practice.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.