Journal ArticleDOI
An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.
TLDR
A loop-locked framework integrating AF diagnose, label query, and model fine-tuning is proposed integrating MIDNN model and the suitability of novel learning strategy for AF and can be extended to other biomedical applications.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2020-04-01. It has received 40 citations till now. The article focuses on the topics: Active learning (machine learning) & Active learning.read more
Citations
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Journal ArticleDOI
Detection of atrial fibrillation using a machine learning approach
Sidrah Liaqat,Kia Dashtipour,Adnan Zahid,Adnan Zahid,Khaled Assaleh,Kamran Arshad,Naeem Ramzan +6 more
TL;DR: A framework for processing the ECG signal in order to determine the AF episodes is developed and the initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance as compared to machine learning classifiers,such as support vectors, logistic regression, etc.
Journal ArticleDOI
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.
TL;DR: In this article, the authors used deep neural networks (DeepNNs) for detecting atrial fibrillation (AF) in electrocardiograms (ECGs) as the main data modality and found that nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling.
Journal ArticleDOI
Review of Deep Learning-Based Atrial Fibrillation Detection Studies.
Fatma Murat,Ferhat Sadak,Ozal Yildirim,Muhammed Talo,Ender Murat,Murat Karabatak,Yakup Demir,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
TL;DR: In this paper, a review of the automated detection of atrial fibrillation using deep learning techniques is presented, focusing on the automated AF detection models developed using DL techniques, including deep neural network, convolutional neural network (CNN), recurrent neural network(RNN), long short-term memory, and hybrid structures.
Journal ArticleDOI
A Novel Interpretable Method Based on Dual-Level Attentional Deep Neural Network for Actual Multilabel Arrhythmia Detection
Yanrui Jin,Jinlei Liu,Yunqing Liu,Chengjin Qin,Zhiyuan Li,Dengyu Xiao,Liqun Zhao,Chengliang Liu +7 more
TL;DR: An accurate and interpretable model for multilabel ECG signals, called dual-level attentional convolutional long short-term memory neural network (DLA-CLSTM), which can improve the accuracy by 22.50% and the F1-macro by 20.51% on average.
Journal ArticleDOI
Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings
TL;DR: In this paper, a novel deep learning classification method, namely, global hybrid multi-scale convolutional neural network (i.e., GH-MS-CNN), is proposed to implement binary classification for Atrial fibrillation (AF) detection.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
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).
Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Journal ArticleDOI
Neocognitron: A Self Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
TL;DR: A neural network model for a mechanism of visual pattern recognition that is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity of their shapes without affected by their positions.
Proceedings ArticleDOI
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
TL;DR: This work designs a method to reuse layers trained on the ImageNet dataset to compute mid-level image representation for images in the PASCAL VOC dataset, and shows that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification.