Automated Neural Network Based Classification of HRV and ECG Signals of Smokers: A Preliminary Study

2015 ◽  
pp. 271-279 ◽  
Author(s):  
Suraj Kumar Nayak ◽  
Ipsita Panda ◽  
Biswajeet Champaty ◽  
Niraj Bagh ◽  
Kunal Pal ◽  
...  
Keyword(s):  
Neural Network ◽  
Ecg Signals ◽  
Preliminary Study

scholarly journals Study of cardiac arrhythmia classification based on Convolutional Neural Network

2020 ◽  
Vol 17 (2) ◽  
pp. 445-458
Author(s):  
Yonghui Dai ◽  
Bo Xu ◽  
Siyu Yan ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Cardiovascular Diseases ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Time Intervals ◽  
Ecg Signals ◽  
Layer Structures ◽  
Time Periods ◽  
The One

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

scholarly journals Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 143-147
Author(s):  
P. Rama Santosh Naidu ◽  
K.Venkata Ramana ◽  
G. Lavanya Devi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dominant Role ◽  
Machine Learning Techniques ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
The Past ◽  
Learning Techniques ◽  
Machine Learning Approach

In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.

Methodology

2018 ◽  
pp. 81-113
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Swarm Intelligence ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Artificial Neural ◽  
Heart Disorders ◽  
Intelligent Methods

This chapter uses intelligent methods based on swarm intelligence and artificial neural network to detect heart disorders based on electrocardiogram signals. This chapter has introduced the methodology undertaken in the denoising, feature extraction, and classification of ECG signals to four heart disorders including the normal heartbeat. It also presents denoising using intelligent methods.

scholarly journals Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

2017 ◽  
Vol 59 ◽  
pp. 326-332 ◽  
Author(s):  
Amir Tjolleng ◽  
Kihyo Jung ◽  
Wongi Hong ◽  
Wonsup Lee ◽  
Baekhee Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cognitive Workload ◽  
Ecg Signals ◽  
Artificial Neural

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 Analysis and classification of cardiac arrhythmia based on general sparsed neural network of ECG signals

2020 ◽  
Vol 2 (7) ◽  
Author(s):  
Sanjay Tanaji Sanamdikar ◽  
Satish Tukaram Hamde ◽  
Vinayak Ganpat Asutkar
Keyword(s):  
Neural Network ◽  
Cardiac Arrhythmia ◽  
Ecg Signals

Classification of Quantitative EEG Data by an Artificial Neural Network: A Preliminary Study

1996 ◽  
Vol 33 (2) ◽  
pp. 106-112 ◽  
Author(s):  
L.A. Riquelme ◽  
B.S. Zanuto ◽  
M.G. Murer ◽  
R.J. Lombardo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Eeg ◽  
Eeg Data ◽  
Preliminary Study ◽  
Artificial Neural

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.

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