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Open accessJournal ArticleDOI: 10.1038/S41598-021-84374-8

Transfer learning for ECG classification.

04 Mar 2021-Scientific Reports (Nature Publishing Group)-Vol. 11, Iss: 1, pp 5251-5251
Abstract: Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to [Formula: see text], effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at .

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8 results found

Open accessJournal ArticleDOI: 10.3390/HEARTS2040037
13 Oct 2021-
Abstract: The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.

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Topics: Beat detection (50%)

1 Citations

Open accessJournal ArticleDOI: 10.1016/J.CONBUILDMAT.2021.125385
Duo Ma1, Jianhua Liu, Hongyuan Fang1, Wang Niannian1  +3 moreInstitutions (2)
Abstract: With the development of deep learning, convolutional neural networks (CNN) have been gradually used in pipeline defeats detection. However, due to the complex environment inside the pipeline, few defeat images are not enough for the training of CNN. A multi-defect detection system based on StyleGAN-SDM and fusion CNN for sewer pipelines is proposed in this paper. First, aiming at the problem of data acquisition and small data volume, raw images are preprocessed by StyleGAN-SDM, which integrates StyleGAN v2 and sharpness discrimination model (SDM) to generate multi-defect images and automatically select clear images. The indexes of Inception-Residual score (IRS), accuracy and macro-F1 score to evaluate the quality of the images generated are 2.968 ± 0.024, 99.64%, and 0.997, respectively. Second, to improve the detection accuracy, a multi-defect classification model (MDCM) based on fusion CNN, which combines Inception network and Residual network, is proposed to classify the on-site images into four categories. Third, compared with conventional deep-learning methods, the mean accuracy and macro-F1 score of the proposed model reach 95.64% and 0.955, which are increased by 1.51% and 0.015 by StyleGAN-SDM, respectively. Finally, to solve the timeliness problem of on-site detection, a real-time multi-defeat detection system for sewer pipelines is established with the computer vision library of OpenCV. Some on-site videos are detected with the mean speed of 24.11 FPS and these results could aid the staff.

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Proceedings ArticleDOI: 10.1109/ICSSE52999.2021.9538448
26 Aug 2021-
Abstract: Nowadays, heart disease has very popularly affected human health. Early diagnosis of heart disease using deep learning networks is a significant task due to possible support for physicians. This paper proposed to build a remote diagnostic system for heart disease via a server. In particular, a patient’s ECG signals are collected from a machine and then sent to the server containing a heart disease classification system using the deep learning network for classifying heart diseases. Furthermore, this real system was designed with a suitable ECG data transmission protocol so that physicians can access the ECG signals and classification results using a smartphone or remote computer. The results obtained from the real experiments in the BME Lab showed that the proposed system operated stably and it can be developed to implement in diagnostic centers or hospitals.

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Open accessPosted Content
Matti Kaisti1, Juho Laitala, Antti AirolaInstitutions (1)
11 Nov 2021-arXiv: Learning
Abstract: We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain randomization where the synthetic signal properties such as the waveform shape, RR-intervals and noise are varied for every training example. Models trained with synthetic data are compared to their counterparts trained with real data. Detection of r-waves in electrocardiograms recorded during different physical activities and in atrial fibrillation is used to compare the models. By allowing the randomization to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data. Experiments show robust performance with different seeds and training examples on different test sets without any test set specific tuning. The method makes possible to train neural networks using practically free-to-collect data with accurate labels without the need for manual annotations and it opens up the possibility of extending the use of synthetic data on cardiac disease classification when disease specific a priori information is used in the electrocardiogram generation. Additionally the distribution of data can be controlled eliminating class imbalances that are typically observed in health related data and additionally the generated data is inherently private.

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Topics: Test set (54%), Synthetic data (53%), Artificial neural network (50%)

Journal ArticleDOI: 10.1016/J.KNOSYS.2021.107473
Fatma Murat1, Ozal Yildirim1, Muhammed Talo1, Yakup Demir1  +5 moreInstitutions (5)
Abstract: Arrhythmia is a condition characterized by perturbation of the regular rhythm of the heart. The development of computerized self-diagnostic systems for the detection of these arrhythmias is very popular, thanks to the machine learning models included in these systems, which eliminate the need for visual inspection of long electrocardiogram (ECG) recordings. In order to design a reliable, generalizable and highly accurate model, large number of subjects and arrhythmia classes are included in the training and testing phases of the model. In this study, an ECG dataset containing more than 10,000 subject records was used to train and diagnose arrhythmia. A deep neural network (DNN) model was used on the data set during the extraction of the features of the ECG inputs. Feature maps obtained from hierarchically placed layers in DNN were fed to various shallow classifiers. Principal component analysis (PCA) technique was used to reduce the high dimensions of feature maps. In addition to the morphological features obtained with DNN, various ECG features obtained from lead-II for rhythmic information are fused to increase the performance. Using the ECG features, an accuracy of 90.30% has been achieved. Using only deep features, this accuracy was increased to 97.26%. However, the accuracy was increased to 98.00% by fusing both deep and ECG-based features. Another important research subject of the study is the examination of the features obtained from DNN network both on a layer basis and at each training step. The findings show that the more abstract features obtained from the last layers of the DNN network provide high performance in shallow classifiers, and weight updates of DNN network also increases the performance of these classifiers. Hence, the study presents important findings on the fusion of deep features and shallow classifiers to improve the performance of the proposed system.

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35 results found

Proceedings ArticleDOI: 10.1109/CVPR.2009.5206848
Jia Deng1, Wei Dong1, Richard Socher1, Li-Jia Li1  +2 moreInstitutions (1)
20 Jun 2009-
Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.

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Topics: WordNet (57%), Image retrieval (54%)

31,274 Citations

Open accessPosted Content
Diederik P. Kingma1, Jimmy Ba2Institutions (2)
22 Dec 2014-arXiv: Learning
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

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23,369 Citations

Open accessProceedings Article
01 Jan 2015-
Abstract: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

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15,992 Citations

Open accessJournal ArticleDOI: 10.1161/01.CIR.101.23.E215
13 Jun 2000-Circulation
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...

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8,656 Citations

Open accessPosted Content
10 Feb 2015-arXiv: Learning
Abstract: Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.

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Topics: Object detection (52%), Visualization (51%)

5,891 Citations

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