scholarly journals A Review on ECG Classification Methods

2020 ◽  
pp. 220-227
Author(s):  
Shijo Easow ◽  
L. C. Manikandan
Keyword(s):  
Cardiovascular Diseases ◽  
Medical Device ◽  
Cardiovascular Disorder ◽  
Main Group ◽  
Ecg Signal ◽  
Classification Methods ◽  
Cardiac Excitability ◽  
Electrocardiogram Ecg

Arrhythmia is a main group of illnesses in cardiovascular disorder and it can occur on its own or with different cardiovascular diseases. The diagnosis of arrhythmia especially depends on the ECG (electrocardiogram). ECG is an important contemporary medical device that records the process of cardiac excitability, transmission, and recovery. The purpose of this study is to classify ECG signal using different methods.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals Interpretable Feature Generation in ECG Using a Variational Autoencoder

2021 ◽  
Vol 12 ◽  
Author(s):  
V. V. Kuznetsov ◽  
V. A. Moskalenko ◽  
D. V. Gribanov ◽  
Nikolai Yu. Zolotykh
Keyword(s):  
Cardiovascular Diseases ◽  
Supervised Learning ◽  
Cardiac Cycle ◽  
Ecg Signal ◽  
Feature Generation ◽  
Maximum Mean Discrepancy ◽  
Variational Autoencoder ◽  
Electrocardiogram Ecg

We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 3.83 × 10−3, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.

scholarly journals A Review Study for Electrocardiogram Signal Classification

2020 ◽  
Vol 4 (1) ◽  
pp. 103
Author(s):  
Lana Abdulrazaq Abdulla ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Electrocardiogram Signal ◽  
Review Study ◽  
Electrocardiogram Ecg

An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

WBAN Based Long Term ECG Monitoring

2013 ◽  
Vol 1 (3) ◽  
pp. 20-37 ◽  
Author(s):  
Marius Rosu ◽  
Sever Pasca
Keyword(s):  
Low Power ◽  
Sensor Nodes ◽  
Ecg Signal ◽  
Area Network ◽  
Wireless Sensor Nodes ◽  
Medication Process ◽  
Remote Healthcare ◽  
Electrocardiogram Ecg ◽  
Supply Information

Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

A novel method for detecting R-peaks in electrocardiogram (ECG) signal

2012 ◽  
Vol 7 (2) ◽  
pp. 118-128 ◽  
Author(s):  
M.Sabarimalai Manikandan ◽  
K.P. Soman
Keyword(s):  
Ecg Signal ◽  
Novel Method ◽  
Electrocardiogram Ecg

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

Sign in / Sign up

Export Citation Format

Share Document