A Soft Computing-Based Measurement System for Medical Applications in Diagnosis of Cardiac Arrhythmias by ECG Signals Analysis

2006 ◽  
Author(s):  
Claudio De Capua ◽  
Stefano De Falco ◽  
Rosario Morello
Keyword(s):  
Soft Computing ◽  
Measurement System ◽  
Cardiac Arrhythmias ◽  
Medical Applications ◽  
Ecg Signals

scholarly journals Novel Stable Capacitive Electrocardiogram Measurement System

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3668
Author(s):  
Chi-Chun Chen ◽  
Shu-Yu Lin ◽  
Wen-Ying Chang
Keyword(s):  
Measurement System ◽  
Input Resistance ◽  
Voltage Divider ◽  
Motion Artifact ◽  
Guard Ring ◽  
Capacitive Sensing ◽  
Ecg Signals ◽  
Optimal Input ◽  
Electrocardiogram Ecg ◽  
Mode Interference

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

IRBM ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 185-194 ◽  
Author(s):  
S. Sahoo ◽  
M. Dash ◽  
S. Behera ◽  
S. Sabut
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmias ◽  
Learning Approach ◽  
Ecg Signals ◽  
Machine Learning Approach

A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals

2022 ◽  
Author(s):  
Yuan Zhang ◽  
Sen Liu ◽  
Zhihui He ◽  
Yuwei Zhang ◽  
Changming Wang
Keyword(s):  
Cardiac Arrhythmias ◽  
Ecg Signals

Compact Temperature Measurement System For Medical Applications

1986 ◽  
Author(s):  
A. M. Scheggi ◽  
M. Bacci ◽  
M. Brenci ◽  
G. Conforti ◽  
R. Falciai ◽  
...  
Keyword(s):  
Temperature Measurement ◽  
Measurement System ◽  
Medical Applications ◽  
Temperature Measurement System

An mHealth System for Monitoring of Children with Suspected Cardiac Arrhythmias

2013 ◽  
Vol 1 (2) ◽  
pp. 54-71
Author(s):  
E. Kyriacou ◽  
D. Hoplaros ◽  
P. Chimonidou ◽  
G. Matheou ◽  
M. Millis ◽  
...  
Keyword(s):  
Communication Networks ◽  
Cardiac Arrhythmias ◽  
Continuous Monitoring ◽  
Environmental Parameters ◽  
Recording Device ◽  
Design Development ◽  
Ecg Signals ◽  
First Case ◽  
The Subject ◽  
Time Acquisition

Arrhythmia is one of the most difficult problems in cardiology and especially in pediatric cardiology. In this study, the authors present a mobile health (m-health) system that can be used for continuous monitoring of children (ages 0-16 years) with suspected cardiac arrhythmias. The system is able to carry-out real-time acquisition and transmission of ECG signals, and facilitate an alarm scheme able to identify possible arrhythmias so as to notify the on-call doctor and the relatives of the child that an event or something that denotes malfunction is happening. In general the problem has been divided into two cases. The first case is called “in-house case”, where the subject is located in his/her house. While the second case is called “moving-patient case”, where the subject might be located anywhere else. The authors’ goal is the continuous 24 hours, monitoring of the child. During the “in house case”, a sensor network installed in the child’s house is used in order to continuously record ECG signals from the patient as well as environmental parameters. The second case is more general. For this case, the child is monitored using the same ECG recording device but the signals are transmitted, through a mobile device, directly to the central monitoring system. The transmission is performed through the use of 2.5G, or 3G, mobile communication networks. The system design, development and technical tests (using an ECG simulator and 20 volunteers) are reported in this paper. The future steps will be the further evaluation of the system on children with suspected cardiac arrhythmias.

Optimizing color reproduction of a topometric measurement system for medical applications

2008 ◽  
Vol 30 (8) ◽  
pp. 1065-1070 ◽  
Author(s):  
Matthias Westhäuser ◽  
Guido Bischoff ◽  
Zoltan Böröcz ◽  
Johannes Kleinheinz ◽  
Gert von Bally ◽  
...  
Keyword(s):  
Measurement System ◽  
Medical Applications ◽  
Color Reproduction

scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

VT/VF Detection Method Based on ECG Signal Quality Assessment

2018 ◽  
Vol 27 (11) ◽  
pp. 1850169 ◽  
Author(s):  
Borisav Jovanović ◽  
Srdan Milenković ◽  
Milan Pavlović
Keyword(s):  
Cardiac Arrhythmias ◽  
Signal Analysis ◽  
Detection Method ◽  
Signal Quality ◽  
Ecg Signals ◽  
Battery Charge ◽  
Time Domain Signal ◽  
Signal Parameters ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Artefacts which are present in electrocardiogram (ECG) recordings distort detection of life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. The method examines single ECG lead and exploits time domain signal parameters for real-time detection of severe cardiac arrhythmias. The method is dedicated to implementation in mobile ECG telemetry systems, which are designed by using low-power microcontrollers, operating more than a week on a single battery charge. The method has been validated on publicly available databases and the results are presented. We verified our method on ECG signals obtained without pre-selection meaning that the noisy intervals were not omitted from signal analysis.

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