Electrocardiogram Device Research Articles

FlexPoints: Efficient electrocardiogram signal compression for machine learning

The electrocardiogram (ECG) stands out as one of the most frequently used medical tests, playing a crucial role in the accurate diagnosis and treatment of patients. While ECG devices generate a huge amount of data, only a fraction of it holds valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the years. However, the rapid development of new machine-learning techniques introduces new challenges. To address this class of problems, we have introduced a FlexPoints algorithm. This innovative algorithm searches for characteristic points on the ECG signal and ignores all other points that lack pertinent medical information. The conducted experiments have demonstrated that our proposed algorithm can significantly reduce the number of data points representing ECG signals without losing valuable medical insights. These sparse but essential characteristic points, referred to as flex points, serve as well-fitted input for modern machine learning models. Such models exhibit enhanced performance when using flex points as input, as opposed to raw data or data compressed by other popular algorithms.

WACARDIA: graphical MATLAB software for Wireless Assessment of CARDiac Interoceptive Accuracy.

Many theories of psychological function emphasize the importance of bodily sensations and the ability to accurately detect them, known as interoceptive accuracy. The most common measure of interoceptive accuracy uses heartbeat detection tasks such as the Whitehead Task, yet to our knowledge there are no freely accessible programs to conduct this task. In this paper, we present novel software called WACARDIA (Wireless Assessment of CARDiac Interoceptive Accuracy), which is free, open-source software that conducts the heartbeat detection task using Matlab and Psychtoolbox. WACARDIA contains several key features supporting participant engagement, operator convenience, and measurement accuracy. First, the program includes an optional practice trial of unlimited duration, a participant-facing graphical interface, and the ability to perform heartbeat detection training. Second, the operator is provided with a graphical user interface, live trial feedback, an accurate wireless electrocardiogram device, and a separate program to conduct the related Heartbeat Tracking task. Finally, the program ensures the accuracy of collected data by scheduling the delivery of tones with high precision and implementing fail-safes to automatically reset erroneous measurements. This paper includes flowcharts that help create transparency by describing our algorithm. We also outline customizable aspects of the program with the intent to have WACARDIA's algorithm expanded to accommodate more situations and applications. With this paper, we hope to encourage the practice of publicizing research software to contribute to the transparency, rigor, and reproducibility of scientific studies. WACARDIA and video tutorials are available at www.github.com/iankleckner/wacardia and http://wacardia.iankleckner.com.

Advancing athlete safety through real-time ECG monitoring for enhanced cardiovascular health in sports performance

This research paper explores the implementation and efficacy of real-time electrocardiogram (ECG) monitoring systems for athletes, emphasizing their potential to significantly enhance safety and performance in sports settings. By utilizing advanced ECG technology, the study investigates how continuous, real-time heart rate and rhythm monitoring can aid in the immediate detection of cardiovascular anomalies during high-intensity activities. The research methodology incorporates the deployment of portable ECG devices in a controlled experimental setup, analyzing data from athletes during training sessions and competitive events. Results from the study highlight the system's ability to provide swift and accurate cardiac assessments, thereby enabling timely medical interventions. Moreover, the paper discusses the technical challenges associated with real-time ECG monitoring, such as signal interference and data accuracy, and addresses privacy and ethical considerations concerning the continuous collection of health data. The discussion extends to the implications of integrating such technology within sports medicine, suggesting that while the systems offer substantial benefits in monitoring and preventing cardiac issues, they also necessitate rigorous standards for data security and ethical oversight. The conclusion advocates for a balanced approach to the adoption of these technologies, proposing future research directions that focus on enhancing system reliability and integrating artificial intelligence to predict potential health risks proactively. This study contributes to the ongoing discourse in sports health technology by providing a comprehensive analysis of real-time ECG monitoring as a transformative tool for athlete healthcare management.

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study

Background and Objectives: Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). Methods: A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland–Altman plot analysis. Results: The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. Conclusions: Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.

ECG of the month

In this issue's ECG of the month, Charles Bloe presents the case of a 37–year-old man whose mobile electrocardiogram device had indicated that he had first-degree heart block.

Implementing a remote self-screening programme for atrial fibrillation using digital health technology among community-dwellers aged 75 years and older: a qualitative evaluation

ObjectiveTo examine the feasibility of implementing remote atrial fibrillation (AF) self-screening among older people supported by a remote central monitoring system.DesignProcess evaluation of the Mass AF randomised clinical trial (ACTRN12621000184875)...

TCT-199 Left Ventricular Systolic Dysfunction Detection by Artificial Intelligence Enhanced 6-Lead Portable Electrocardiogram Device: A Prospective Single-Center Cohort Study

TCT-199 Left Ventricular Systolic Dysfunction Detection by Artificial Intelligence Enhanced 6-Lead Portable Electrocardiogram Device: A Prospective Single-Center Cohort Study

Arduino-Based Real-Time ECG Monitoring System for Heart Signal Visualization

Electrocardiogram (ECG) monitoring is an essential tool in healthcare for diagnosing and tracking heart conditions. However, existing ECG devices are often expensive, limiting accessibility for personal or educational use. This project aims to develop a low-cost ECG monitoring system using an Arduino UNO, AD8232 sensor, and TFT SPI display. The system collects ECG signals via electrodes and displays the real-time PQRST waveform on the TFT screen and the Arduino IDE serial monitor. Resistors are utilized to ensure signal clarity by reducing electrical noise. The project successfully demonstrated the ability to visualize clear ECG signals in real time, offering a practical and cost-effective solution for continuous cardiac monitoring

FPGA‐Based Implementation of Real‐Time Cardiologist‐Level Arrhythmia Detection and Classification in Electrocardiograms Using Novel Deep Learning

ABSTRACTCardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design.

A randomised controlled implementation study integrating patient self-screening with a remote central monitoring system to screen community-dwellers aged 75 years and older for atrial fibrillation.

Diagnosis of atrial fibrillation (AF) provides opportunities to reduce stroke risk. This study aimed to compare AF diagnosis rates, participant satisfaction and feasibility of an electrocardiogram (ECG) self-screening virtual care system with usual care. This randomised controlled implementation study involving community-dwelling people aged ≥75 years was conducted from May 2021 to June 2023. Participants were given a handheld single-lead ECG device and trained to self-record ECGs once daily on weekdays for 12 months. The control group received usual care with their general practitioners in the first 6 months and participated in the subsequent 6 months. AF diagnosis and participant satisfaction were assessed at 6 months. 200 participants (mean age 79.0±3.4 years; 54.0% female; 72.5% urban). AF was diagnosed in 10/97 (10.3%) intervention participants and 2/100 (2.0%) in the control group (Odds Ratio 5.6, 95% CI 1.4-37.3, p=0.03). In the intervention, 80% of AF cases were diagnosed within 3 months. 91/93 (97.9%) intervention participants and 55/93 (59.1%) control-waitlisted participants (p<0.001) were satisfied with AF screening. Of the expected 20 days per month, the overall monthly median number of days participants self-recorded ECGs was 20 (interquartile range 17-22). Participants were confident using the device (93%), reported it was easy to use (98%) and found screening efficient (96%). Patient-led AF self-screening using single-lead ECG devices with a remote central monitoring system was feasible, acceptable, and effective in diagnosing AF among older people. This screening model could be adapted for implementation, interfacing with integrated care models within existing health systems.

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study: reply

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study: reply

The NORwegian atrial fibrillation self-SCREENing (NORSCREEN) trial: rationale and design of a randomized controlled trial.

Atrial fibrillation (AF) is a common arrhythmia, and many cases of AF may be undiagnosed. Whether screening for AF and subsequent treatment if AF is detected can improve long-term outcome remains an unsettled question. The primary aim of the NORwegian atrial fibrillation self-SCREENing (NORSCREEN) trial is to assess whether self-screening for AF with continuous electrocardiogram (ECG) for 3-7 days in individuals aged 65 years or older with at least one additional risk factor for stroke, and initiation of guideline-recommended therapy in patients with detected AF, will reduce the occurrence of stroke. This study is a nationwide open, siteless, randomized, controlled trial. Individuals ≥65 years of age are randomly identified from the National Population Register of Norway and are invited to take a digital inclusion/exclusion test. Individuals passing the inclusion/exclusion test are randomized to either the intervention group or the control group. A total of 35 000 participants will be enrolled. In the intervention group, self-screening is performed continuously over 3-7 days at home with a patch ECG device (ECG247) at inclusion and after 12-18 months. If AF is detected, guideline-recommended therapy will be initiated. Patients will be followed up for 5 years through national health registries. The primary outcome is time to a first stroke (ischaemic or haemorrhagic stroke). The first participant in the NORSCREEN trial was enrolled on 1 September 2023. The results from the NORSCREEN trial will provide new insights regarding the efficacy of digital siteless self-screening for AF with respect to stroke prevention in individuals at an increased risk of stroke. Clinical trials: NCT05914883.

Increasing the reach: optimizing screening for atrial fibrillation-the STROKESTOP III study.

Atrial fibrillation (AF) is the most common type of cardiac arrythmia and is an important risk factor for ischaemic stroke. Many cases of AF remain undiagnosed due to its paroxysmal, intermittent, and often asymptomatic nature. Early detection of AF through screening and initiation of treatment with oral anticoagulants can prevent stroke, increase life expectancy, and decrease the cost of healthcare for the society. However, participation has been low in previous AF screening studies employing population screening. The aim of this study is to determine whether opportunistic screening is a superior method to increase participation in comparison to population screening. We hypothesize that opportunistic screening will significantly increase participation. In our study, STROKESTOP III, a randomized prospective cohort study, we compare two different methods of AF screening in high-risk individuals: population screening vs. opportunistic screening. Sixteen different primary clinics in Värmland, Sweden, serving 75-76-year-old individuals (n = 2954), will be randomized to either population screening or opportunistic screening. The individuals will be instructed to record electrocardiogram (ECG) for 30 s, 3 times daily for 2 weeks, using a handheld one-lead ECG device. Patients with detected AF will be referred to their primary healthcare physician and offered treatment. The main objective of the study is to determine the rate of participation in opportunistic screening in comparison to population screening. The STROKESTOP III study will provide valuable information on which screening method to use for improved participation in atrial fibrillation screening.

Patient Driven EKG Device Performance in Adults with Fontan Palliation.

The aim of this study was to evaluate the accuracy of the KardiaMobile (KM) device in adults with a Fontan palliation, and to assess the KM function as a screening tool for atrial arrhythmias. While patient driven electrocardiogram (EKG) devices are becoming a validated way to evaluate cardiac arrhythmias, their role for patients with congenital heart disease is less clear. Patients with single ventricle Fontan palliation have a high prevalence of atrial arrhythmias and represent a unique cohort that could benefit from early detection of atrial arrhythmias. This single center prospective study enrolled adult patients with Fontan palliation to use the KM heart rhythm monitoring device for both symptomatic episodes and asymptomatic weekly screening over a 1-year period. Accuracy was assessed by comparing the automatic KM interpretation (KM-auto) to an electrophysiologist overread (KM-EP) and traditional EKG. Fifty patients were enrolled and 510 follow-up transmissions were received. The sensitivity and specificity of enrollment KM-auto compared to EKG was 65% and 100%, respectively. The sensitivity and specificity of enrollment KM-auto compared to the KM-EP was 75% and 96%, respectively. In the adult Fontan palliation, the accuracy of the KM device to detect a normal rhythm was reliable and best with a physician overread. Abnormal or uninterpretable KM-auto device interpretations, symptomatic transmissions, and any transmissions with a high heart rate compared to a patient's normal baseline should warrant further review.

Feasibility and accuracy of mobile QT interval monitoring strategies in bedaquiline-enhanced prophylactic leprosy treatment.

Some anti-mycobacterial drugs are known to cause QT interval prolongation, potentially leading to life-threatening ventricular arrhythmia. However, the highest leprosy and tuberculosis burden occurs in settings where electrocardiographic monitoring is challenging. The feasibility and accuracy of alternative strategies, such as the use of automated measurements or a mobile electrocardiogram (mECG) device, have not been evaluated in this context. As part of the phase II randomized controlled BE-PEOPLE trial evaluating the safety of bedaquiline-enhanced post-exposure prophylaxis (bedaquiline and rifampicin, BE-PEP, versus rifampicin, SDR-PEP) for leprosy, all participants had corrected QT intervals (QTc) measured at baseline and on the day after receiving post-exposure prophylaxis. The accuracy of mECG measurements as well as automated 12L-ECG measurements was evaluated. In total, 635 mECGs from 323 participants were recorded, of which 616 (97%) were of sufficient quality for QTc measurement. Mean manually read QTc on 12L-ECG and mECG were 394 ± 19 and 385 ± 18 ms, respectively (p < 0.001), with a strong correlation (r = 0.793). The mean absolute QTc difference between both modalities was 11 ± 10 ms. Mean manual and automated 12L-ECG QTc were 394 ± 19 and 409 ± 19 ms, respectively (n = 636; p < 0.001), corresponding to moderate agreement (r = 0.655). The use of a mECG device for QT interval monitoring was feasible and yielded a median absolute QTc error of 8 ms. Automated QTc measurements were less accurate, yielding longer QTc intervals.

Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms.

Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results. In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively. A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Accuracy of a Single-Lead ECG Device for Diagnosis of Cardiac Arrhythmias Compared Against Cardiac Electrophysiology Study

Single-lead electrocardiogram (ECG) devices may allow detection and diagnosis of cardiac rhythms. However, data on their accuracy for detecting cardiac arrhythmias beyond atrial fibrillation are limited. We aimed to determine the accuracy of the AliveCor KardiaMobile (AC) (AliveCor Inc, Mountain View, CA, USA) for the diagnosis of arrhythmias against gold standard cardiac electrophysiology study (EPS). Patients undergoing clinically indicated EPS underwent simultaneous rhythm recording with an AC, standard 12-lead ECG, and EP catheters for intracardiac electrograms. Rhythms recorded during EPS were classified based on electrogram, 12-lead ECG, and clinical findings. Blinded reviewers provided differential diagnoses for the single-lead AC tracings; a separate reviewer compared diagnoses made between the AC tracings and EPS findings. In 49 patients, 843 cardiac rhythms were captured during 502 AC recordings. Analysis of tracings containing sinus rhythm (n=273) returned an overall accuracy of 92%, with sensitivity and specificity values of 93% and 92%, respectively. Accuracy for tracings per rhythm was atrial fibrillation 91% (n=51); supraventricular tachycardia accuracy was 89% (n=191), ventricular tachycardia 91% (n=198), ventricular fibrillation 98% (n=11), and asystole 100% (n=5). Accuracy for supraventricular ectopy was 93% (n=28) and for premature ventricular complexes was 91% (n=86). Overall accuracy was 94% for solitary rhythms and 93% in tracings from patients with baseline bundle branch block. When compared against the gold standard EPS diagnosis, the interpretation of arrhythmias recorded by an AliveCor single-lead ECG device had reasonable diagnostic accuracy.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation.

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Ureido-Ionic Liquid Mediated Conductive Hydrogel: Superior Integrated Properties for Advanced Biosensing Applications.

Ionic conductive hydrogels (ICHs) have recently gained prominence in biosensing, indicating their potential to redefine future biomedical applications. However, the integration of these hydrogels into sensor technologies and their long-term efficacy in practical applications pose substantial challenges, including a synergy of features, such as mechanical adaptability, conductive sensitivity, self-adhesion, self-regeneration, and microbial resistance. To address these challenges, this study introduces a novel hydrogel system using an imidazolium salt with a ureido backbone (UL) as the primary monomer. Fabricated via a straightforward one-pot copolymerization process that includes betaine sulfonate methacrylate (SBMA) and acrylamide (AM), the hydrogel demonstrates multifunctional properties. The innovation of this hydrogel is attributed to its robust mechanical attributes, outstanding strain responsiveness, effective water retention, and advanced self-regenerative and healing capabilities, which collectively lead to its superior performance in various applications. Moreover, this hydrogel exhibited broad-spectrum antibacterial activity. Its potential for biomechanical monitoring, especially in tandem with contact and noncontact electrocardiogram (ECG) devices, represents a noteworthy advancement in precise real-time cardiac monitoring in clinical environments. In addition, the conductive properties of the hydrogel make it an ideal substrate for electrophoretic patches aimed at treating infected wounds and consequently enhancing the healing process.

Low-cost electrocardiogram monitoring system for elderly people using LabVIEW

Cardiovascular diseases increase due to factors such as obesity, an inadequate diet, and are a problem due to shortages of medical personnel and hospitals. In this case, the implementation of technological solutions is presented as a necessity to prevent heart diseases. Various approaches are used to design low-cost electrocardiogram (ECG) devices, from the use of Bluetooth technology to facilitate data transmission, to the development of wearable ECG devices that use artificial intelligence. The objective is to develop a monitoring system in LabVIEW to visualize the heart rhythms of older adults in the city of Lima (Peru), focusing on ease of use and adaptation to their needs, with the purpose of collaboration between health professionals. A development approach is used that encompasses design, implementation, and iterative testing, as well as practical evaluations and pilot testing. As a result, the correct functioning of the ECG device was validated. Electronic components and electrodes were integrated into the board to capture cardiac signals, energized with batteries and sending the information to an interface in LabVIEW. In conclusion, a portable ECG device has been developed that uses operational amplifiers (Op-amps) and analog filters to reduce noise in cardiac measurements and an intuitive interface in LabVIEW