Improving Cloud-based ECG Monitoring, Detection and Classification using GAN

2020 ◽  
pp. 42-49
Author(s):  
admin admin ◽  
◽  
◽  
Monika Gupta
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Healthcare Applications ◽  
Ecg Signals ◽  
Cardiac Abnormalities ◽  
Adversarial Networks ◽  
Data Points

Internet of Things (IoT) based healthcare applications have grown exponentially over the past decade. With the increasing number of fatalities due to cardiovascular diseases (CVD), it is the need of the hour to detect any signs of cardiac abnormalities as early as possible. This calls for automation on the detection and classification of said cardiac abnormalities by physicians. The problem here is that, there is not enough data to train Deep Learning models to classify ECG signals accurately because of sensitive nature of data and the rarity of certain cases involved in CVDs. In this paper, we propose a framework which involves Generative Adversarial Networks (GAN) to create synthetic training data for the classes with less data points to improve the performance of Deep Learning models trained with the dataset. With data being input from sensors via cloud and this model to classify the ECG signals, we expect the framework to be functional, accurate and efficient.

scholarly journals Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229951 ◽  
Author(s):  
Atsushi Teramoto ◽  
Tetsuya Tsukamoto ◽  
Ayumi Yamada ◽  
Yuka Kiriyama ◽  
Kazuyoshi Imaizumi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks

scholarly journals Improving ECG Classification Using Generative Adversarial Networks

2020 ◽  
Vol 34 (08) ◽  
pp. 13280-13285
Author(s):  
Tomer Golany ◽  
Gal Lavee ◽  
Shai Tejman Yarden ◽  
Kira Radinsky
Keyword(s):  
Learning Algorithms ◽  
Cardiac Activity ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Cardiac Events ◽  
Ecg Signals ◽  
Adversarial Networks ◽  
Classifier Performance ◽  
Supervised Learning Algorithms

The Electrocardiogram (ECG) is performed routinely by medical personell to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms. Numerous supervised learning algorithms were proposed, requiring manual feature extraction. Lately, deep neural networks were also proposed for this task for reaching state-of-the-art results. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations and the low amount of training data available for each arrhythmia are challenging for deep learning algorithms, and impede generalization. In this work, the use of generative adversarial networks is studied for the synthesis of ECG signals, which can then be used as additional training data to improve the classifier performance. Empirical results prove that the generated signals significantly improve ECG classification.

Analysis and application of generative-adeversarial networks for producing high quality images

Ergodesign ◽  
2020 ◽  
Vol 2020 (4) ◽  
pp. 167-176
Author(s):  
Yuriy Malakhov ◽  
Aleksandr Androsov ◽  
Andrey Averchenkov
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
High Quality ◽  
Adversarial Networks ◽  
Network Operation

The article discusses generative adversarial networks for obtaining high quality images. Models, architecture and comparison of network operation are presented. The features of building deep learning models in the process of performing the super-resolution task, as well as methods associated with improving performance, are considered.

scholarly journals Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 4590
Author(s):  
Yunpeng Yue ◽  
Hai Liu ◽  
Xu Meng ◽  
Yinguang Li ◽  
Yanliang Du
Keyword(s):  
Deep Learning ◽  
High Precision ◽  
Ground Penetrating Radar ◽  
Data Augmentation ◽  
Least Square ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Radar Images ◽  
Adversarial Networks ◽  
Ground Penetrating

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

scholarly journals GENERATING SYNTHETIC TRAINING DATA FOR OBJECT DETECTION USING MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 443-449
Author(s):  
Y. Lin ◽  
K. Suzuki ◽  
H. Takeda ◽  
K. Nakamura
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rapid Development ◽  
Real Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Traffic Signs ◽  
Adversarial Networks ◽  
The Neural Network

Abstract. Nowadays, digitizing roadside objects, for instance traffic signs, is a necessary step for generating High Definition Maps (HD Map) which remains as an open challenge. Rapid development of deep learning technology using Convolutional Neural Networks (CNN) has achieved great success in computer vision field in recent years. However, performance of most deep learning algorithms highly depends on the quality of training data. Collecting the desired training dataset is a difficult task, especially for roadside objects due to their imbalanced numbers along roadside. Although, training the neural network using synthetic data have been proposed. The distribution gap between synthetic and real data still exists and could aggravate the performance. We propose to transfer the style between synthetic and real data using Multi-Task Generative Adversarial Networks (SYN-MTGAN) before training the neural network which conducts the detection of roadside objects. Experiments focusing on traffic signs show that our proposed method can reach mAP of 0.77 and is able to improve detection performance for objects whose training samples are difficult to collect.

scholarly journals Conditional Generative Adversarial Networks for Commonsense Machine Comprehension

2017 ◽  
Author(s):  
Bingning Wang ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Reading Comprehension ◽  
Deep Learning ◽  
State Of The Art ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Feature Engineering ◽  
Language Understanding ◽  
Story Reading ◽  
Testing Period ◽  
Adversarial Networks

Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.

scholarly journals Unsupervised feature learning-based encoder and adversarial networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Endang Suryawati ◽  
Hilman F. Pardede ◽  
Vicky Zilvan ◽  
Ade Ramdan ◽  
Dikdik Krisnandi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Learning ◽  
Object Classification ◽  
Learning By Doing ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Good Representation ◽  
Learning Methods ◽  
Adversarial Networks ◽  
Unsupervised Deep Learning

AbstractIn this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.

GANDaLF: GAN for Data-Limited Fingerprinting

2021 ◽  
Vol 2021 (2) ◽  
pp. 305-322
Author(s):  
Se Eun Oh ◽  
Nate Mathews ◽  
Mohammad Saidur Rahman ◽  
Matthew Wright ◽  
Nicholas Hopper
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Large Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Closed World ◽  
Training Samples ◽  
A Site

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

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