scholarly journals Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring

2022 ◽  
Vol 12 (2) ◽  
pp. 1540
Author(s):  
Sugondo Hadiyoso ◽  
Heru Nugroho ◽  
Tati Latifah Erawati Rajab ◽  
Kridanto Surendro
Keyword(s):  
Real Data ◽  
Ecg Monitoring ◽  
Mesh Topology ◽  
Regression Imputation ◽  
Data Prediction ◽  
Zigbee Protocol ◽  
Em Method ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg ◽  
Incorrect Data

The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors.

A Systematic Review of Noninvasive Electrocardiogram Monitoring Devices for the Evaluation of Suspected Cardiovascular Syncope

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Monica Solbiati ◽  
Lucia Trombetta ◽  
Roberto M. Sacco ◽  
Luca Erba ◽  
Viviana Bozzano ◽  
...  
Keyword(s):  
Systematic Review ◽  
Technological Advancement ◽  
Ecg Monitoring ◽  
Review Of The Literature ◽  
Future Studies ◽  
External Loop ◽  
Artery Disease ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices

The aims of this study were (1) to identify research publications studying noninvasive electrocardiogram (ECG) monitoring devices, (2) to define and categorize current technology in noninvasive ECG recording, and (3) to discuss desirable noninvasive recording features for personalized syncope evaluation to guide technological advancement and future studies. We performed a systematic review of the literature that assessed noninvasive ECG-monitoring devices, regardless of the reason for monitoring. We performed an Internet search and corresponded with syncope experts and companies to help identify further eligible products. We extracted information about included studies and device features. We found 173 relevant papers. The main reasons for ECG monitoring were atrial fibrillation (n = 45), coronary artery disease (n = 10), syncope (n = 8), palpitations (n = 8), other cardiac diseases (n = 67), and technological aspects of monitoring (n = 35). We identified 198 devices: 5 hospital telemetry devices, 12 patches, 46 event recorders, 70 Holter monitors, 23 external loop recorders, 20 mobile cardiac outpatient telemetries, and 22 multifunctional devices. The features of each device were very heterogeneous. There are a large number of ECG-monitoring devices with different features available in the market. Our findings may help clinicians select the appropriate device for their patients. Since there are only a few published articles analyzing their usefulness in syncope patients, further research might improve their use in this clinical setting.

Reduction of Motion Artifacts In Electrocardiogram Monitoring Using an Optical Sensor

2011 ◽  
Vol 45 (2) ◽  
pp. 155-163 ◽  
Author(s):  
Yan Liu ◽  
Michael G. Pecht
Keyword(s):  
Optical Sensor ◽  
Motion Artifacts ◽  
False Alarms ◽  
Ecg Monitoring ◽  
New Device ◽  
Ambulatory Ecg Monitoring ◽  
Inappropriate Treatment ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg ◽  
Ecg Artifacts

Abstract The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts, which can trigger false alarms, cause misdiagnoses, and lead to inappropriate treatment decisions. Skin stretch associated with patient motion is the most significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable, measured noninvasively using an optical sensor incorporated into an ECG electrode. The results demonstrate that this new device and method can significantly reduce motion induced ECG artifacts in continuous ambulatory ECG monitoring.

scholarly journals TRIMETAZIDINE MR EFFECTS ON THE PARAMETERS OF 24-HOUR ELECTROCARDIOGRAM MONITORING AND HEART RATE VARIABILITY

2013 ◽  
Vol 12 (5) ◽  
pp. 68-74
Author(s):  
M. V. Novikova ◽  
M. G. Glezer
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Test ◽  
Ecg Monitoring ◽  
Randomised Study ◽  
St Segment ◽  
Number Of Patients ◽  
Stable Coronary Heart Disease ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg

Aim. To assess the effects of the four-month trimetazidine MR therapy on the parameters of 24-hour electrocardiogram (ECG) monitoring and heart rate variability (HRV) in patients with stable coronary heart disease (CHD).Material and methods. This prospective, non-randomised study, with the inclusion of 66 consecutive patients who had stable CHD and stable stress test results, investigated the effects of trimetazidine MR therapy on the parameters of 24-hour ECG monitoring and HRV.Results. Trimetazidine MR did not markedly affect the 24-hour, daytime, or nighttime levels of heart rate. Trimetazidine MR therapy was not associated with any substantial changes in frequency and time-domain HRV parameters or in the incidence of cardiac arrhythmias. However, there was a significant reduction in the number of patients with ST segment depression (from 66,7% to 43,8%; p<0,001) and in the duration of ischemic episodes (from 10 (6,2;21) minutes to 7,42 (5;12,3) minutes (p=0,025)).Conclusion. Adding trimetazidine MR to the treatment of patients with stable CHD provides an additional beneficial antiischemic effect.

System for Continuous and Prolonged Ambulatory ECG Monitoring with Hosting and Visualization on the Cloud

2021 ◽  
Vol 10 (1) ◽  
pp. 57
Author(s):  
Daniel Cuevas-González ◽  
Juan Pablo García-Vázquez ◽  
Miguel Bravo-Zanoguera ◽  
Roberto López-Avitia ◽  
Marco A. Reyna ◽  
...  
Keyword(s):  
Health Care ◽  
Sampling Rate ◽  
Cloud Services ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Ambulatory Ecg ◽  
Ambulatory Ecg Monitoring ◽  
Access Information ◽  
Electrocardiogram Ecg ◽  
Small File

In this paper, we propose investigating the ability to integrate a portable Electrocardiogram (ECG) device to commercial platforms to analyze and visualize information hosted in the cloud. Our ECG system based on the ADX8232 microchip was evaluated regarding its performance of recordings of a synthetic ECG signal for periods of 1, 2, 12, 24, and 36 h on six different cloud services to investigate whether it maintains reliable ECG records. Our results show that there are few cloud services capable of 24 h or longer ECG recordings. But some existing services are limited to small file sizes of less than 1,000,000 lines or 100 MB, or approximately 45 min of an ECG recording at a sampling rate of 360 Hz, making it difficult an extended time monitoring. Cloud platforms reveal some limitations of storage and visualization in order to provide support to health care specialists to access information related to a patient at any time.

Development of a Home-based Fetal Electrocardiogram (ECG) Monitoring System

2021 ◽  
Author(s):  
Sadaf Sarafan ◽  
Tai Le ◽  
Floranne Ellington ◽  
Zhijie Zhang ◽  
Michael P. H. Lau ◽  
...  
Keyword(s):  
Monitoring System ◽  
Ecg Monitoring ◽  
Fetal Electrocardiogram ◽  
Home Based ◽  
Electrocardiogram Ecg

scholarly journals A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Minggang Shao ◽  
Zhuhuang Zhou ◽  
Guangyu Bin ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Monitoring ◽  
Web Browser ◽  
Ecg Signals ◽  
Cloud Server ◽  
Android App ◽  
Electrocardiogram Ecg ◽  
The Web ◽  
Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM

2012 ◽  
Vol 12 (03) ◽  
pp. 1250049 ◽  
Author(s):  
MOHD AFZAN OTHMAN ◽  
NORLAILI MAT SAFRI
Keyword(s):  
Ventricular Arrhythmia ◽  
Ventricular Arrhythmias ◽  
Normal Sinus Rhythm ◽  
High Sensitivity ◽  
Real Data ◽  
Biological Behavior ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Semantic Mining ◽  
Electrocardiogram Ecg

Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.

Design of Visibility Forecasting System on Freeways Based on the Internet of Things

2011 ◽  
Vol 48-49 ◽  
pp. 406-409
Author(s):  
Guang Bing Xiao ◽  
Yong Jun Min ◽  
Wei Yi Cai
Keyword(s):  
Internet Of Things ◽  
Real Data ◽  
Forecasting Model ◽  
The Internet ◽  
Physical Processes ◽  
Zigbee Protocol ◽  
Forecasting System ◽  
Background Fields ◽  
Visibility Forecasting ◽  
The Internet Of Things

This paper aims at designing a visibility forecasting system on freeways based on the Internet of Things. Physical processes of fog formation on freeways are explored by the related real data and circulation background fields, and then some characteristics are presented through the forecasting model. The paper introduces the Zigbee protocol into the hardware design of the system, in which the epigenous machines can analyze the received data dynamically.

A Digital Twin for Intra-Logistics Process Planning for the Automotive Sector Supported by Big Data Analytics

2019 ◽  
Author(s):  
Guilherme Guerreiro ◽  
Paulo Figueiras ◽  
Ruben Costa ◽  
Maria Marques ◽  
Diogo Graça ◽  
...  
Keyword(s):  
Big Data ◽  
Digital Simulation ◽  
Big Data Analytics ◽  
Real Data ◽  
3D Models ◽  
Related Data ◽  
Data Prediction ◽  
Prediction Techniques ◽  
Eu Project ◽  
Logistics Process

Abstract One of the areas that can heavily benefit with Industry 4.0 is the logistics, namely with the association of sensing technologies and the application of techniques such as Big Data Analytic, Data Visualization, prediction algorithms, and especially 3D simulation. The association of real data, prediction techniques, and 3D models, allow the creation of realistic Digital Twins that emulate factory processes, making possible the experimentation and testing of new ideas and different scenarios by tweaking key variables, without stopping production. However, there are many challenges in order to handle and compute all fast-growing, multi dimension data generated, so that all this production related data can be quickly used for defect control, preventive maintenance, advanced analytics for production and resources management, or even later simulation. The work presented in this paper focus in this “in between” processing work, presenting an easily deployable and self-reconfigurable Big Data architecture, where different technologies can work together to extract, transform, load, apply analytics, and then feed a 3D Digital Simulation model. The work presented in this paper is funded by the EU project BOOST4.0 and focus in a specific logistic process of car manufacturing.

scholarly journals Textile Electrocardiogram (ECG) Electrodes for Wearable Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1013 ◽  
Author(s):  
Katya Arquilla ◽  
Andrea Webb ◽  
Allison Anderson
Keyword(s):  
Health Monitoring ◽  
Human Subjects ◽  
Rank Test ◽  
Ecg Monitoring ◽  
Sample Mean ◽  
High Data ◽  
Textile Electrodes ◽  
Significant Difference ◽  
Student’S T ◽  
Electrocardiogram Ecg

Wearable health-monitoring systems should be comfortable, non-stigmatizing, and able to achieve high data quality. Smart textiles with electronic elements integrated directly into fabrics offer a way to embed sensors into clothing seamlessly to serve these purposes. In this work, we demonstrate the feasibility of electrocardiogram (ECG) monitoring with sewn textile electrodes instead of traditional gel electrodes in a 3-lead, chest-mounted configuration. The textile electrodes are sewn with silver-coated thread in an overlapping zig zag pattern into an inextensible fabric. Sensor validation included ECG monitoring and comfort surveys with human subjects, stretch testing, and wash cycling. The electrodes were tested with the BIOPAC MP160 ECG data acquisition module. Sensors were placed on 8 subjects (5 males and 3 females) with double-sided tape. To detect differences in R peak detectability between traditional and sewn sensors, effect size was set at 10% of a sample mean for heart rate (HR) and R-R interval. Paired student’s t-tests were run between adhesive and sewn electrode data for R-R interval and average HR, and a Wilcoxon signed-rank test was run for comfort. No statistically significant difference was found between the traditional and textile electrodes (R-R interval: t = 1.43, p > 0.1; HR: t = −0.70, p > 0.5; comfort: V = 15, p > 0.5).

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