scholarly journals Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application

2020 ◽  
Vol 1 (3) ◽  
pp. 106-111 ◽  
Author(s):  
Dathar Hasan ◽  
Ayad Ismaeel
Keyword(s):  
Monitoring System ◽  
Healthcare System ◽  
Medical Devices ◽  
Human Population ◽  
Continuous Monitoring ◽  
Heart Diseases ◽  
Medical Expenditure ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Monitoring Process

Nowadays, heart diseases are considered to be the primary reasons for unexpected deaths. Thus, various medical devices have been developed by engineers to diagnose and scrutinize various diseases. Healthcare has become one of the most substantial issues for both individuals and government due to brisk growth in human population and medical expenditure. Many patients suffer from heart problems causing some critical threats to their life, therefore they need continuous monitoring by a traditional monitoring system such as Electrocardiographic (ECG) which is the most important technique used in measuring the electrical activity of the heart, this technique is available only in the hospital which is very costly and far for remote patients. The development of wireless technologies enables to build a network of connected devices via the internet. The proposed ECG monitoring system consists of AD8382 ECG sensor to read patient's data, Arduino Uno, ESP8266 Wi-Fi module, and IoT Blynk application. The implementation of the proposed ECG healthcare system enables the doctor to monitor the patient's remotely using IoT Blynk application installed on his smartphone for processing and visualizing the patient's ECG signal. The monitoring process can be done at any time and anywhere without the need for the hospital.

scholarly journals A wearable system for real-time outpatient ECG monitoring

2019 ◽  
pp. 26-30
Author(s):  
T.O. Білобородова ◽  
І.С. Скарга-Бандурова ◽  
В.С. Дерев’янченко
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Monitoring System ◽  
Morphological Feature ◽  
Well Being ◽  
Wearable Devices ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Systems Architecture

Functional state of the cardiovascular system is an important factor for human physical well-being. To perform analysis of the cardiovascular state, the wearable continuous ECG monitoring system is essential. In this paper, a wearable ECG monitoring system based on IoT is proposed. The systems architecture is presented. Wearable devices design employs few optimal components for the acquisition of acceptable ECG signal. The R peaks corresponding to each heartbeat, and T waves, a morphological feature of the ECG are detected. It enables to perform heart rate and heart rate variability analyses, as well as  extract, store and analyze  the long term ECG measurements.

scholarly journals Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1509 ◽  
Author(s):  
Chien-Chin Hsu ◽  
Bor-Shing Lin ◽  
Ke-Yi He ◽  
Bor-Shyh Lin
Keyword(s):  
Myocardial Ischemia ◽  
Monitoring System ◽  
Mechanical Design ◽  
Heart Diseases ◽  
Signal Quality ◽  
Ecg Signal ◽  
Ventricular Premature Contraction ◽  
Dry Electrodes ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg

A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction.

IoT based Low Power Wearable ECG Monitoring System

2019 ◽  
Vol 14 (1) ◽  
pp. 68-74 ◽  
Author(s):  
A.S. Kulkarni ◽  
M. Suchetha ◽  
N. Kumaravel
Keyword(s):  
Cardiac Arrest ◽  
Monitoring System ◽  
High Performance ◽  
Electrical Potential ◽  
Battery Life ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Large Area ◽  
Cardiac Status ◽  
Key Aspects

Background: An electrocardiogram device monitors the cardiac status of a patient by recording the heart’s electrical potential vs time. Such devices play a very important role to save the life of patients who survive a heart attack or suffer from these patients. An early detection of conditions that lead to the onset of cardiac arrest allows doctors to provide proper treatment on time and prevents death or disability from cardiac arrest. Most developing countries have very poor information about these health care issues. Methods: An actual deployment of the system was used to evaluate key aspects of the system architecture, in particular, the possibility to monitor the ECG signal of single patients in a large area and for a long time the possibility to access ECG data through the web interface. The test deployment consisted of ECG sensor AD8232, wi-fi module and IoT server. The IoT server was installed on a Linux/ windows machine. The wifi has been configured to connect to the server, through an ADSL router. Conclusion: We have proposed a wireless wearable ECG monitoring system enabled with an IoT platform that integrates heterogeneous nodes of ECG sensor and applications, has a long battery life and provides a high-quality ECG signal. The system allows monitoring single/multiple patients on a relatively large indoor area (home, building, nursing home, etc). As observed, this result is obtained through a careful set of choices at the level of components, circuit solutions, and algorithms. We would like to stress the fact that a dedicated overall output is not enough to achieve an advantage in terms of overall sensor performance. The latter depends on the optimization of the whole sensor. Indeed, this proposed ECG sensor, based on a high-performance ADC and an arm processor, provides much better performance, in terms of power consumption and noise, than many proposed system based on a purposely designed front-end chip.

scholarly journals Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2916 ◽  
Author(s):  
Xiaowen Xu ◽  
Ying Liang ◽  
Pei He ◽  
Junliang Yang
Keyword(s):  
Wavelet Transform ◽  
Heart Diseases ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Monitoring Systems ◽  
Ecg Signal ◽  
Wavelet Thresholding ◽  
Ecg Monitoring ◽  
Ecg Signals ◽  
Empirical Wavelet Transform

Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.

scholarly journals Development of Electrocardiograph Monitoring System

2018 ◽  
Vol 150 ◽  
pp. 01013 ◽  
Author(s):  
Khairul Affendi Rosli ◽  
Mohd. Hafizi Omar ◽  
Ahmad Fariz Hasan ◽  
Khairil Syahmi Musa ◽  
Mohd Fairuz Muhamad Fadzil ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Monitoring System ◽  
Digital Signal ◽  
Diagnostic Tools ◽  
Instrumentation Amplifier ◽  
Time Interval ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Matlab Software

Electrocardiograph (ECG) monitoring system is one of the diagnostic tools which can help in reduce the risk of heart attack. A cardiologist may be able to determine heart condition from the ECG signal that recorded from subject. The purpose was to design an ECG monitoring system which consists of ECG circuit and digital signal processing system to deny the unwanted signal. In general, the ECG signal is nature weak and only around 1mV amplitude. Therefore filter and amplifier circuits were designed into 3 stages with a total gain of 1000 to bring the signal to around 1V. Circuit designed included of instrumentation amplifier, bandpass filter and notch filter. The frequency bandwidth of ECG is between 0.05Hz until 100Hz. Schematic circuit was tested by software simulation before proceeding to hardware implementation. Simulation analysis was done by using Software Proteus 8 Professional while the further signal processing was done in MATLAB software environment. A PQRST ECG waveform can be seen clearly after digital filtering stage in MATLAB environment. Digital signal processing in MATLAB software included of pre-filtering, Fast Fourier transform and peak detection. As conclusion, the time interval between peaks can be determined automatically which can provide useful information in clinical aspect.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

2021 ◽  
pp. 095441192199810
Author(s):  
Madhwendra Nath ◽  
Subodh Srivastava ◽  
Niharika Kulshrestha ◽  
Dilbag Singh
Keyword(s):  
Success Rate ◽  
Heart Sound ◽  
Heart Diseases ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Shannon Energy ◽  
Detection And Localization ◽  
Pcg Signals ◽  
Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

scholarly journals Special Issue: ECG Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 651
Author(s):  
Florent Baty
Keyword(s):  
Monitoring System ◽  
Ecg Monitoring ◽  
Special Issue ◽  
Short Overview

This editorial of the Special Issue “ECG Monitoring System” provides a short overview of the 13 contributed articles published in this issue [...]

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