Remote Electrocardiogram Monitoring Research Articles

Design of ECG Circuit and Patient Remote Monitoring System Using MQTT Protocol for ECG Signals

In the healthcare sector, leveraging the Internet of Things (IoT) brings significant advantages, especially in remote monitoring of patients' health using electrocardiogram (ECG) signals. This study employs IoT technology to enable remote ECG monitoring, utilizing the MQTT (Message Queuing Telemetry Transport) protocol to establish a web server. The dedicated measuring device integrates the ADAS1000-3 IC with ESP32 for real-time ECG signal acquisition. In this study, we utilize the Pan-Tompkins algorithm to extract QRS complexes from electrocardiographic signals, count heartbeats, and early detect cardiovascular abnormalities. This solution was tested on a sample dataset from the MIT-BIH (Massachusetts Institute of Technology - Beth Israel Deaconess Medical Center) database. Finally, the article proposes to design and develop a device using advanced IC technologies such as IC ADAS1000-3 combined with ESP32. This device will be designed to meet the requirements of real-time processing and data transmission via webserver.

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

Remote electrocardiogram monitoring in cancer patients for detecting cardiotoxicity. Case report

Cardiotoxicity is the direct adverse effect of anticancer agents on the function or structure of the heart, occurring both during treatment and many years later. The manifestations of cardiotoxicity can be very different: heart failure, coronary heart disease, cardiomyopathy, myocarditis, pericarditis, and arrhythmias, including atrial fibrillation (AF). The incidence of AF in the cancer patient population during therapy is 20–30%. Current options of remote electrocardiogram monitoring ensure the timely detection and treatment of AF in cancer patients, including the administration of anticoagulants. The article presents a clinical case of a 59-year-old patient with squamous cell carcinoma of the lower third of the esophagus after the first course of the TPF polychemotherapy regimen (docetaxel, cisplatin, 5-fluorouracil) with AF detected using a single-channel electrocardiogram monitor in a smartphone case.

Early Warning System of Sudden Cardiac Death Based on Internet Electrocardiograph and Intelligent Platform

An early warning system of sudden cardiac death based on the Internet electrocardiograph and intelligent platform is designed to detect the signal of sudden cardiac death in time and save lives. The system is mainly composed of four parts: Internet electrocardiograph, mobile terminal, intelligent platform and 120 emergency center. It has been verified that the sudden cardiac death early warning system is reliable, suitable for remote electrocardiogram monitoring, and can provide diagnosis and early warning for people at risk of sudden cardiac death. The system realizes the miniaturization of ECG monitoring equipment, and has the advantages of intelligent diagnosis and rapid warning, and can be applied to the pre-hospital monitoring of high-risk groups of sudden cardiac death and has a good application value.

Telemedicine technologies and remote ECG monitoring. Reality and prospects. Functionalist perspective. Part 2

The second part of the review is devoted to remote methods of recording an electrocardiogram (ECG), with an emphasis on the modern options of this technology. The article reflects the methods of ECG registration, their technical difficulties and solutions. The review is intended for specialists in the field of functional diagnostics, presents the current state of the problem of remote ECG (DM EKG) monitoring and will be of interest to healthcare organizers in decision-making when purchasing equipment in medical institutions of the country.

Application of remote electrocardiogram monitoring systems in chest pain centers for patients with high-risk chest pain.

Chinese chest pain centers (CPCs) have been expanding and maturing for the past decade, but patient wait times for pre-hospital care remain long. To demonstrate that the remote electrocardiogram (ECG) monitoring system can ensure more efficient treatment for patients with ST-elevation myocardial infarction (STEMI) in CPCs, we compared patients with high-risk chest pain who used remote ECG monitoring systems to those who used conventional ECGs in retrospective cohort study. Based on the inclusion and exclusion criteria, 290 patients who visited our CPC between June 2019 and March 2022 with acute chest pain and a diagnosis of STEMI as well as patients who had undergone an emergency primary percutaneous coronary intervention were selected. Among them, 73 patients with STEMI had employed remote real-time dynamic 12-lead ECG monitoring devices, while 217 patients with STEMI (i.e., the controls) had used conventional ECG monitoring. The effectiveness of treatment procedures for the two groups was investigated. As statistical measures, the symptom onset-to-wire times, first medical contact (FMC)-to-wire times, door-to-wire times, major adverse cardiac events in hospital, and the troponin T levels were analyzed. Compared with the control group, the patients with remote real-time dynamic 12-lead ECG monitoring devices showed shorter times for both symptom onset-to-wire (234.8 ± 95.8 min vs. 317.6 ± 129.6 min, P= 0.0321) and from symptom onset-to-FMC (170.5 ± 86.3 min vs. 245.3 ± 115.6min, P= 0.0287); this group also had a lower 30-day mortality rate (2.73% vs. 4.14%, P= 0.003). The differences between the two groups were statistically significant (P< 0.05). With remote real-time dynamic 12-lead ECG monitoring equipment, myocardial ischemia can be treated more quickly, leading to fewer possible cardiac events and a better prognosis.

ECG Monitoring Using Deep Learning – A Review

Electrocardiogram (ECG) is used to detect heart's rhythm and electrical activity. Heart rhythm problems (heart arrhythmias) occur when the electrical signals that coordinate the heart's beats don't work properly and faulty signaling causes the heart to beat too fast, too slow or irregularly. Patients facing arrhythmia have no indications of having an arrhythmia and only a doctor will be able to diagnose arrhythmias in a routine test. Therefore, low cost portable monitoring system plays a important role. The main objective of this project is to design and develop a model for predicting arrhythmia (atrial fibrillation) along with monitoring the ECG signals. To create an arrhythmia prediction model and an ECG monitoring system, Deep Learning algorithms, TensorFlow and Keras library are applied here. The system is being designed with Raspberry pi 3, Arduino UNO, AD8232 single lead ECG sensor, biomedical sensor pad and battery. This system will be helpful for remote ECG monitoring and make easier for doctors to monitor the ECG of their patients outside the hospital. Key Words: ECG, Deep Learning, arrhythmia.

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction.

The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.

A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System

This article presents a power-efficient fully analog classifier architecture for the detection of critical cardiac abnormalities, i.e., asystole, extreme bradycardia, and tachycardia. To reduce power consumption and hardware complexity, an analog QRS complex detection circuit and arithmetic counter-based classification modules are introduced. The QRS detection circuit is autonomous and consumes an average current of 34 nA only, vis-à-vis state-of-the-art QRS detection designs that are digital signal processor (DSP) assisted and consume tens of microwatts of power. Furthermore, a heart-rate estimator provides the number of QRS complexes per minute. Each of the proposed modules is successfully validated through real electrocardiogram (ECG) test signals taken from the PhysioNet database. The proposed beat detector circuit exhibits a sensitivity of 97.85% and a positive prediction of 98.3%. Experimental results based on bedside monitor data show that the proposed classification module provides an overall sensitivity of 96.25% and a positive prediction of 96.97%. The complete classification architecture, implemented fully on analog platform, is simulated in the UMC 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and consumes 119-nW power with an active silicon area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$450\times 800~\mu \text{m}$ </tex-math></inline-formula> . Moreover, the low-power implementation makes it suitable for long-term battery-operated remote ECG monitoring systems.

Effective compression and classification of ECG arrhythmia by singular value decomposition

Electrocardiogram (ECG) monitoring systems are widely applied to tele-cardiology healthcare programs nowadays, where ECG signals should always be compressed first during its transmission and storage. Previous studies attempted to achieve high quality decompressed signal with compression ratio as high as possible. In this paper, we investigated the performance on ECG arrhythmia classification on ECG signal decompressed after lossy compression with a high compression ratio. We proposed a simple but efficient method utilizing singular value decomposition (SVD) to decompose ECG signals, then applied the decompressed data to a convolutional neural network (CNN) and supporting vector machine (SVM) for classification. Using the optimization method with accuracy and compression ratio as objective functions, the highest average accuracy obtained is above 96% when the selected number of singular value is only 3. The evaluation results illustrated that the decompressed ECG signal even with a relatively high distortion can still achieve a satisfying performance in the arrhythmia classification.Thus,we proved that the real-time nature of the remote mobile ECG monitoring system can be greatly improved and countless people who are in need of ECG diagnosis can benefit from it.

CSNet: A deep learning approach for ECG compressed sensing

Remote electrocardiogram (ECG) monitoring plays a very important role in the prevention and treatment of cardiovascular diseases. However, the current long-term ECG monitoring generates a large amount of data, which puts great pressure on the bandwidth and transmission systems. Compressed sensing (CS) has great attraction for resource-limited wearable devices used in remote ECG monitoring. Traditional CS reconstruction algorithms often require complex signal processing and prior knowledge, and the reconstruction process is time-consuming, which limits the application of CS in remote ECG monitoring systems. This paper proposes a non-iterative fast reconstruction algorithm based on CS and deep learning, which combines convolutional neural network (CNN) and long short-term memory (LSTM) to directly learn the mapping relationship between the rising dimension signal of the measurements and the original signal, and is validated in the MIT-BIH Arrhythmia Database (MITDB). The experimental results show that the reconstruction error of this method is lower than that of traditional algorithms including basis pursuit (BP), orthogonal matching pursuit (OMP), bound-optimization-based block sparse Bayesian learning (BSBL-BO) and rotate-singular value decomposition + basis pursuit (R-SVD+BP) at high compression ratios (CRs, when CR⩾50%). At the same time, for a 30-min ECG signal, only about 0.12 s is needed to complete the reconstruction, which is at least 45 times faster than the traditional algorithms, which is enough to support real-time applications. In addition, the proposed method is also validated in MIT-BIH Normal Sinus Rhythm Database (NSRDB), MIT-BIH Atrial Fibrillation Database (AFDB) and European ST-T Database (EDB), and the reconstructed signals of NSRDB and AFDB meet the clinical requirements at CR⩽70%, and the reconstructed signals of EDB meet the clinical requirements at CR⩽90%.

IoT-enabled cloud-based real-time remote ECG monitoring system

Statistical reports all around the world have deemed cardiovascular diseases (CVDs) as the largest contributor to the death count. The electrocardiogram (ECG) is a widely accepted technology employed for investigation of CVDs of the person. The proposed solution deals with an efficient internet of things (IoT) enabled real-time ECG monitoring system using cloud computing technologies. The article presents a cloud-centric solution to provide remote monitoring of CVD. Sensed ECG data are transmitted to S3 bucket provided by Amazon web service (AWS) through a mobile gateway. AWS cloud uses HTTP and MQTT servers to provide data visualisation, quick response and long-live connection to device and user. Bluetooth low energy (BLE 4.0) is used as a communication protocol for low-power data transmission between device and mobile gateway. The proposed system is implemented with filtering algorithms to ignore distractions, environmental noise and motion artefacts. It offers an analysis of ECG signals to detect various parameters such as heartbeat, PQRST wave and QRS complex intervals along with respiration rate. The proposed system prototype has been tested and validated for reliable ECG monitoring remotely in real-time.

Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study.

BackgroundDue to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19.ObjectiveThe primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians.MethodsIn this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician.ResultsOur approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM.ConclusionsOur proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.

Design and Implementation of a Low Cost Wireless Ambulatory ECG Monitoring System for Deployment in Rural Communities

Due to the rising number of heart patients and the apparent need for more robust electrocardiogram (ECG) monitoring of these patients, hospitals are increasingly investing in typical cloud technology or centralized hospital server based remote ECG monitoring systems. However, the deployment these systems in rural communities is limited due to the high cost factor. To counter this challenge, in this paper, we focus on the design and implementation of a low cost real time wireless ambulatory ECG monitoring system. The detected ECG signals are first filtered and amplified and then digitally converted by a microcontroller. The digitized ECG signals are then sent over a ZigBee wireless link to a gateway personal computer (PC) at patient’s premises. The received ECG data from the ZigBee connection is displayed in real time via the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) user interface on the PC for instant personalized evaluation of the ECG data. The ECG data can be saved on the PC and sent via email to a remote cardiologist or a clinician. Additionally, the gateway PC at patient’s end acts as web server for sharing patient’s data over the Internet. The remote off-site physician (medical staff in a hospital) can use a web browser on a PC, laptop or a mobile phone with Internet connection to access patient’s real time ECG trace for monitoring, expert review and diagnosis. It is shown that the system prototype allows users to acquire reliable ECG signals effectively and simply. The proposed ambulatory ECG system offers an alternative low cost deployment strategy and is especially suited for remote cardiac monitoring of patients in rural communities.

P1453Safe multidisciplinary management of new therapies: the ritmo project (real time continuous web monitoring)

Abstract Background Fingolimod is an immunosuppressive drug that prevents the typical autoimmune responses of multiple sclerosis (MS). This drug can cause, even after the first dose, bradycardia and atrioventricular conduction block (AVB). According to a decree of the Committee for Medicinal Products for Human Use (CHMP), the first administration of a dose of fingolimod must meet the following conditions: a) 12-lead electrocardiogram (ECG) and blood pressure (BP) evaluations before first dose administration; b) continuous ECG and BP monitoring every hour during the first 6 hours of treatment. The aim of the study was to evaluate the efficacy of our virtual Intensive Care Unit connected to selected Departments of Neurology for cardiology monitoring via web, during the first dose administration of fingolimod in patients with MS. Methods Health Telematic Network, in collaboration with our Cardiology Department, has installed in several Neurology Departments in the country intensive care equipment with real time monitoring via the web of ECG, BP and oximetry. The telemonitoring system is composed of an IntelliVue MP2 monitor, for 12-lead ECG recording, and by an IntelliVue Philips M3150 Central, for continuous remote monitoring of ECG and the detection and storage of arrhythmias. The HTN Cardiologist thus can evaluate in real time ECG monitored and each alarm detected. Results From Jan 2014 to Dec 2018, 172 sites participated and 6041 patients were enrolled. The telemonitoring system recognized 5116 episodes of bradycardia, 297 first degree AVB, 213 second degree AVB Mobitz I, 48 second degree AVB Mobitz II, 618 pathological lengthening of the QTc interval, single monomorphic ventricular ectopic beats in 266 patients, bigeminy ventricular extrasystoles in 15 patients and polymorphic ventricular extrasystoles in 3 cases. 6 cases of nonsustained ventricular tachycardia occurred and were recognized. Conclusion This web-based real time telemonitoring project was an optimal solution for the administration of the first dose of fingolimod, according to current indications of the CHMP. The RITMO project could lead the way for the use of a multidisciplinary approach for the management of new therapies.

Effects of home-based cardiac exercise rehabilitation with remote electrocardiogram monitoring in patients with chronic heart failure: a study protocol for a randomised controlled trial

IntroductionPatients with chronic heart failure (CHF) can benefit from exercise rehabilitation (ER) with significant improvements in exercise capacity, quality of life and reduction in hospitalisations. Despite its reported benefits, only...

Application of wireless remote electrocardiogram monitoring device in atrial fibrillation patients undergoing radiofrequency catheter ablation

Application of wireless remote electrocardiogram monitoring device in atrial fibrillation patients undergoing radiofrequency catheter ablation

A remote electrocardiogram monitoring system with good swiftness and high reliablility

To solve the problems of long time delay and low reliability in existing systems, a Two-level Load-balance Monitoring Strategy (TLLBMS) is proposed in this paper. In monitoring terminal level, based on customizable System-on-Chip A2F500, monitoring terminals implement parallel Principal Component Analysis and Independent Component Analysis (PCA-ICA) algorithm to fulfil quickly preprocessing and feature extraction of electrocardiogram (ECG) signals, and give a pre-classification and alarming mechanism. Depending on the results of pre-classification, monitoring terminals transmit only eigenvectors to monitoring center at a Dynamically Variable Time Interval (DVTI). This will reduce the quantity of data transmission dramatically, which means it will lead to high robustness of communication and short time delay. In monitoring center level, monitoring server processes eigenvectors directly, and realizes quick classification, diagnosis and rescuing. To ensure the correctness of communication and protect data from being falsified, Message Digest Algorithm MD5 is realized to verify the integrity of data.

Rule-Based Method for Morphological Classification of ST Segment in ECG Signals

Abnormal change in ST morphology is an important indicator for heart disease, especially for myocardial ischemia. The automatic classification of ST morphology can provide valuable information for helping physicians in the diagnosis of myocardial ischemia, especially in long-term and remote electrocardiogram (ECG) monitoring environments. In order to provide more accurate and efficient ST morphology classification for long-term and remote ECG monitoring applications, a simple rule-based ST morphology classification method is proposed that identifies ST segments with the normal morphology type and five abnormal morphology sub-types: concave or convex elevation, up-sloping, down-sloping, or horizontal depression. The proposed method consists of the following steps: (1) 0.05–45 Hz band-pass filtering; (2) signal quality assessment; (3) R peak detection; (4) removal of ECG beats with anomalous RR interval; (5) identification of start and end points of ST segment; (6) determination of sliding baseline; and (7) rule-based morphological classification of each ST segment. A total of 17,314 ECG beats were selected from the European ST–T ECG database, with 11,461 beats used for training and 5853 beats used for testing. Four quantitative parameters, namely sensitivity, positive predictive rate, F-measure, and G-mean, were used as classification indices. The final classification accuracy for the training and testing data are 91.8 and 90.1 % respectively, indicating the clinical significance of the proposed method. This method is clinically and pathophysiologically important, and is an important contribution to the early detection of the potential risk of myocardial ischemia in heart disease patients in long-term and remote ECG monitoring.