scholarly journals Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1827
Author(s):  
Dengao Li ◽  
Hang Wu ◽  
Jumin Zhao ◽  
Ye Tao ◽  
Jian Fu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Public Database ◽  
Ecg Signals ◽  
The World ◽  
Neural Network Structure ◽  
Test Sets ◽  
Gated Recurrent Units

Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

scholarly journals Deep Neural Network Based Real Time Multi-Class Arrhythmia Classification in IoT-Cloud Platform

2021 ◽  
Author(s):  
AKHILA NAZ K A ◽  
R S Jeena ◽  
P Niyas
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Time Signal ◽  
Ecg Signals ◽  
Proposed Model ◽  
Heart Beating ◽  
Life Threatening ◽  
Manual Processing

Abstract An arrhythmia is a condition which represents irregular beating of the heart, beating of the heart too fast, too slow, or too early compared to a normal heartbeat. Diagnosis of various cardiac conditions can be done by the proper analysis, detection, and classification of life-threatening arrhythmia. Computer aided automatic detection can provide accurate and fast results when compared with manual processing. This paper proposes a reliable and novel arrhythmia classification approach using deep learning. A Deep Neural Network (DNN) with three hidden layers has been developed for arrhythmia classification using MIT-BIH arrhythmia database. The network classifies the input ECG signals into six groups: normal heartbeat and five arrhythmia classes. The proposed model was found to be very promising with an accuracy of 99.45 percent. The real time signal classification and the application of internet of things (IOT) are the other highlights of the work.

scholarly journals Deep Learning-Based Approach for Diagnosis of COVID-19 Patients from CT Lung Images

2021 ◽  
Vol 31 (2) ◽  
pp. 27-34
Author(s):  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Structure ◽  
Experimental Results ◽  
Second Step ◽  
The World ◽  
Segmented Images ◽  
Life Threatening ◽  
Neural Network Structure

COVID-19 is currently one of the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection plays an important role in identifying, making better decisions as well as ensuring treatment for the patients. In this study, we propose an approach to identify COVID-19 patients via deep learning methods. The proposed approach includes two steps: segmentation of lung and classification of patients via analyzing the segmented images. Especially, in the first step, the attention gate is integrated into Unet neural network structure, which can help increase the accuracy of the segmentation tasks. In the second step, the EfficientNet-B4 is applied to classify COVID-19 patients. By using EfficientNet, the performance of the classification process as well as the size of the model are improved. We apply the proposed approach for the database including 130 patients. Experimental results show the desired performances of the proposed approach.

scholarly journals ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1121
Author(s):  
Sandra Śmigiel ◽  
Krzysztof Pałczyński ◽  
Damian Ledziński
Keyword(s):  
Neural Network ◽  
Network Architectures ◽  
Main Field ◽  
Successful Result ◽  
Convolutional Network ◽  
Ecg Signals ◽  
Learning Techniques ◽  
Disease Entities ◽  
Test Sets

The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Sign in / Sign up

Export Citation Format

Share Document