ECG Interpretation Research Articles

Testing the accuracy of automatic ECG-annotation for suspected acute coronary syndrome

Abstract Background Correct interpretation of ECG recordings plays a crucial role in cardiology. ECG recordings are performed immediately in order to guide treatment of patients with suspected acute coronary syndrome (ACS). Non-cardiologists usually rely on automated manufacturer-derived ECG interpretation. The main purpose of these methods is to detect lifethreatening ECG patterns with high sensitivity, at cost of specificity. However, as yet, the accuracy of these automatic annotations has not been tested in investigator-initiated (IIT) clinical studies. Purpose To test the accuracy of automated ECG annotation provided by the largest ECG manufacturer in Germany in patients undergoing coronary angiography (CA), because of biomarker-positive suspected ACS. Methods We retrospectively identified patients presenting with suspected ACS to two large tertiary hospitals between 2014 – 2021. Inclusion criteria were defined as indication for CA because of biomarker-positive suspected ACS and availability of ECG raw-data. Patients with ST-elevation myocardial infarction were excluded from this analysis. All ECGs were classified as non-indicative or indicative for ischaemia, according to automatic annotations provided by the ECG-manufacturer. The primary endpoint of the study was type 1 myocardial infarction requiring revascularization. Secondary endpoints were identification of coronary artery disease (CAD) with or without revascularization and intrahospital mortality. The association of ECG-abnormalities with the primary and secondary endpoints was tested using logistic-regression analysis. Confidence intervals were calculated using bootstrapping. Results We screened 9,598 patients for eligibility and finally included 2,073 patients meeting the inclusion und exclusion criteria. The median age was 73 years (IQR 61 – 80), 661 (32%) of the patients were females. Among those patients 1,299 (63%) were diagnosed with type 1 myocardial infarction requiring revascularization, 475 patients (23%) didn’t show signs of CAD and 153 patients (7%) died during the index hospital stay. Automatic annotation of ECG-abnormalities was significantly associated with the primary endpoint (OR 1.36; 95% CI 1.14 – 1.63; p < 0.001), and secondary endpoints (OR 1.72; 95% CI 1.39 – 2.13; p < 0.001 for CAD and 1.61; 95% CI 1.16-2.24; p = 0.005 for mortality). The overall sensitivity, specificity and negative predictive value of the method was 47% (45-50%), 60% (57-63%), and 41% (38-43%), respectively. The sensitivity and specificity for different subgroups is illustrated in Figure 1. Conclusion Current manufacturer algorithms for automatic ECG-annotation show a statistically significant association with CAD requiring revascularization. However, 59% of the patients requiring revascularization are not identified with the annotated ECG-abnormalities. Application of artificial intelligence might improve these algorithms and allow safe application of the method in clinical practice.Figure 1

Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram.

Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction. This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and random forest) were then used for analysis to find risk features using a random 70/30% split for discovery and validation cohorts. Five-year SCD occurrence rate was 3.3% (n = 287). Regardless of the used ML algorithm, the most significant risk ECG risk features detected by the CIE included known risk features such as QRS duration and factors associated with QRS duration, heart rate-corrected QT time (QTc), and the presence of premature ventricular contractions (PVCs). Risk score formed by using most significant CIE features associated with the risk of SCD despite adjusting for any clinical risk factor (including left ventricular ejection fraction). Sensitivity of CIE data to correctly identify patients with high risk of SCD (over 10% 5-year risk of SCD) was usually low, but specificity and negative prediction value reached up to 96.9% and 97.3% when selecting only the most significant features identified by logistic regression modeling (p-value threshold <0.01 for accepting features in the model). Overall, CIE data showed a modest overall performance for identifying high risk individuals with area under the receiver operating characteristic curve values ranging between 0.652 and 0.693 (highest for extreme gradient boosting and lowest for logistic regression). This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.

Fine Tuning ECG Interpretation for Young Athletes: ECG Screening Using Z-score-based Analysis

BackgroundElectrocardiograms (ECGs) in athletes commonly reveal findings related to physiologic adaptations to exercise, that may be difficult to discern from true underlying cardiovascular abnormalities. North American and European societies have published consensus statements for normal, borderline, and abnormal ECG findings for athletes, but these criteria are not based on established correlation with disease states. Additionally, data comparing ECG findings in athletes to non-athlete control subjects are lacking. Our objective was to compare the ECGs of collegiate athletes and non-athlete controls using Z-scores for digital ECG variables to better identify significant differences between the groups and to evaluate the ECG variables in athletes falling outside the normal range.MethodsValues for 102 digital ECG variables on 7206 subjects aged 17–22 years, including 672 athletes, from Hawaii Pacific Health, University of Hawaii, and Rady Children’s Hospital San Diego were obtained through retrospective review. Age and sex-specific Z-scores for ECG variables were derived from normal subjects and used to assess the range of values for specific ECG variables in young athletes. Athletes with abnormal ECGs were referred to cardiology consultation and/or echocardiogram.ResultsAthletes had slower heart rate, longer PR interval, more rightward QRS axis, longer QRS duration but shorter QTc duration, larger amplitude and area of T waves, prevalent R’ waves in V1, and higher values of variables traditionally associated with left ventricular hypertrophy (LVH): amplitudes of S waves (leads V1-V2), Q waves (V6, III) and R waves (II, V5, V6). Z-scores of these ECG variables in 558 (83%) of the athletes fell within − 2.5 and 2.5 range derived from the normal population dataset, and 60 (8.9%) athletes had a Z-score outside the − 3 to 3 range. While 191 (28.4%) athletes met traditional voltage criteria for diagnosis of LVH on ECG, only 53 athletes (7.9%) had Z-scores outside the range of -2.5 to 2.5 for both S amplitude in leads V1-V2 and R amplitude in leads V5-6. Only one athlete was diagnosed with hypertrophic cardiomyopathy with a Z-score of R wave in V6 of 2.34 and T wave in V6 of -5.94.ConclusionThe use of Z-scores derived from a normal population may provide more precise screening to define cardiac abnormalities in young athletes and reduce unnecessary secondary testing, restrictions and concern.

TCT-594 AI-powered STEMI Network: First Prospective Implementation of Smartphone-based ECG Interpretation System Assisting Emergent Cathlab Activation

TCT-594 AI-powered STEMI Network: First Prospective Implementation of Smartphone-based ECG Interpretation System Assisting Emergent Cathlab Activation

A quality improvement project for the ECG interpretation skills of junior doctors in West Suffolk Hospital

A quality improvement project for the ECG interpretation skills of junior doctors in West Suffolk Hospital

Lecture-based versus case-based e-learning in the improvement of ECG interpretation skills in junior medical trainees; a prospective cohort study

Lecture-based versus case-based e-learning in the improvement of ECG interpretation skills in junior medical trainees; a prospective cohort study

Associated Factors and Mortality of Arrhythmia in Emergency Department: A Narrative Review.

Cardiac arrhythmias represent a major concern in the emergency department (ED), particularly given their association with significant morbidity and mortality. This narrative review examines the various factors influencing arrhythmias and their impact on patient outcomes in emergency settings. Managing complex supraventricular and ventricular arrhythmias (VAs) during acute myocardial infarction (AMI) and severe cardiovascular conditions remains challenging, despite advancements in diagnostic and therapeutic techniques. Ventricular arrhythmias frequently forecast worse outcomes during hospital stays and heighten the chances of sudden cardiac death and cardiac arrest, especially within the initial 30 days after a heart attack. The incidence of arrhythmias in ED is increasing due to demographic changes and higher rates of chronic illnesses such as diabetes, hypertension, and chronic kidney disease. These comorbidities, coupled with lifestyle factors such as smoking and alcohol consumption, complicate arrhythmia management, especially among older adults and males. Rapid and precise ECG interpretation in the ED is crucial for identifying specific arrhythmia types and initiating appropriate treatments. Atrial fibrillation (AF), the most prevalent form of rapid heart rhythm originating above the ventricles, notably impacts patient outcomes, particularly in cases of AMIand heart failure. In the ED, managing AFfocuses on preventing strokes with thromboprophylaxis and employing risk assessment tools such as CHA2DS2-VASc and HAS-BLED scores. The analysis highlights how risk factors like hypertension, obesity, obstructive sleep apnea (OSA), and diabetes intricately influence the development and worsening of AF. Optimizing AF treatment outcomes requires a multidisciplinary approach involving cardiologists, emergency physicians, and critical care specialists. Future research should prioritize evaluating the effectiveness of preventive and therapeutic interventions for AF, integrating new risk factors and genetic insights to enhance prediction and management strategies. Understanding the factors contributing to arrhythmias and mortality in the ED underscores the importance of timely and accurate diagnostic and therapeutic measures to improve patient care and outcomes.

A deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes

BackgroundDetection of atrial tachyarrhythmias (ATA) on long-term ECG recordings is a pre-requisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep Learning (DL) algorithms provide improved performances on resting ECG databases. However, results on long-term Holter recordings are scarce. ObjectiveWe aimed to build and evaluate a DL modular software using ECG features well known to cardiologists with a user interface that allows cardiologists to adjudicate the results and drive a second DL analysis. MethodsUsing a large (n=187 recordings, 249,419 one-minute samples), beat-to-beat annotated, two-lead Holter database, we built a DL algorithm with a modular structure mimicking expert physician ECG interpretation to classify atrial rhythms. The DL network includes 3 modules (cardiac rhythm regularity, electrical atrial waveform, and raw voltage by time data) followed by a decision network and a long-term weighting factor . The algorithm was validated on an external database. ResultsF1 scores of our classifier were 99% for ATA detection, 95% for Atrial fibrillation and 90% for Atrial flutter respectively. Using the external MIT-database, the classifier obtains F1-score of 97% for the NSR class and 96% for the ATA class. Residual errors could be corrected by manual deactivation of one module in 7/15 of the recordings with an accuracy < 90%. ConclusionA DL modular software using ECG features well known to cardiologists provided an excellent overall performance. Clinically significant residual errors were most often related to the classification of the atrial arrhythmia type (fibrillation vs. flutter). The modular structure of the algorithm helped to edit and correct the AI-based first-pass analysis and will provide a basis for explainability.

Detection and classification of electrocardiography using hybrid deep learning models

ObjectiveElectrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. MethodsPrecision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared. ResultsThe classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07 and 1.87, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record. ConclusionWhen compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.

Rationale and Design of the Australasian Registry of Screening ECGs in National Athletes Project.

Cardiac screening of elite athletes is widely recommended by Australasian sporting federations, but data are not structured to be shared. Data are lacking from underrepresented groups to inform ECG interpretation guidelines. The ARENA (Australasian Registry of Screening ECGs in National Athletes) project is a retrospective and prospective, multicenter, longitudinal, observational registry of athlete cardiac screening results and outcomes. The aim is to create a repository to improve our understanding of the diagnoses and outcomes of screening. Participating sports that conduct cardiac screening of athletes will contribute data. This includes an initial collection (retrospective data, waiver of consent) and future prospective data (opt-out consent). Data include sex, age, sport/event, screening date, ECG findings, cardiac test results, follow-up details, sport participation status, cardiac diagnoses, and major cardiovascular outcomes defined as sudden cardiac arrest/death, cardiac syncope or implanted cardioverter defibrillator shock, cardiac hospitalization, and arrhythmias requiring intervention. Comparisons will be made between diagnoses, outcomes, and ECG features and analyzed by sport and sex. The ARENA project was developed in collaboration with sporting bodies, team physicians, and players association representatives and endorsed by the Australasian College of Sport & Exercise Physicians and Sports Medicine Australia. The ARENA project will provide a long-term international data repository to improve our understanding of ECG interpretation, cardiac screening and diagnoses, and the prevalence of cardiovascular outcomes in screened athletes. A unique aim is to address evidence gaps in underrepresented athlete groups, specifically female athletes and Indigenous populations. Results will inform screening policies and guidelines.

Knowledge, practice, and factors associated with electrocardiography interpretation among nurses working in adult emergency and critical care units at selected governmental hospitals in Addis Ababa, Ethiopia, 2023: a cross-sectional study

BackgroundElectrocardiography (ECG) is a noninvasive diagnostic method used to examine electrical and muscular cardiac activity. It is usually the first-line diagnostic tool for patients with chest pain. Late recognition of electrocardiography abnormalities can cause in-hospital cardiac arrest, further complicate care, increase the cost of health care, and prolong admission. Hence, the objective of this study was to determine the knowledge, practice, and associated factors of nurses regarding the interpretation of electrocardiography.MethodsA cross-sectional study was carried out on 334 nurses working at randomly selected public hospitals from March 06, 2023, to April 05, 2023. Since our study populations were less than the calculated sample size, all nurses who fulfilled the inclusion criteria were included. The collected data were coded and cleaned using EpiData version 4.2 and analysed using Statistical Package for the Social Sciences (SPSS) version 25. Both bivariate and multivariate logistic regression was run to find an association between dependent and independent variables. Odds ratios (ORs) with 95% confidence levels were used to measure the strength of associations.ResultsA total of 334 nurses responded to the survey, yielding a 93% response rate. Only 15.4% and 9% of them had satisfactory knowledge and good practice, respectively. Nurses who had 2–4 years of experience in the current unit were 3.13 times more likely (AOR = 3.13 (1.23, 7.92) to have sufficient knowledge. Nurses working in cardiac care units were 3.45 times more likely to have good practices than those working in intensive care units (AOR = 3.45 (1.02, 13.73)).ConclusionThe study revealed that educational qualifications, in-service training, and current working units were significantly associated with nurses’ knowledge and practice of electrocardiography interpretation. The concerned body must provide continuous on-the-job training related to ECG interpretation to all nurses working in adult emergency and critical care unit.

Direct Comparison of Methods to Differentiate Wide Complex Tachycardias: Novel Automated Algorithms Versus Manual ECG Interpretation Approaches.

Differentiating wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia via 12-lead ECG interpretation is a crucial but difficult task. Automated algorithms show promise as alternatives to manual ECG interpretation, but direct comparison of their diagnostic performance has not been undertaken. Two electrophysiologists applied 3 manual WCT differentiation approaches (ie, Brugada, Vereckei aVR, and VT score). Simultaneously, computerized data from paired WCT and baseline ECGs were processed by 5 automated WCT differentiation algorithms (WCT Formula, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). The diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. A total of 212 WCTs (111 VT and 101 supraventricular wide tachycardia) from 104 patients were analyzed. WCT Formula demonstrated superior accuracy (85.8%) and specificity (87.1%) compared with Brugada (75.2% and 57.4%, respectively) and Vereckei aVR (65.3% and 36.4%, respectively). WCT Formula II achieved higher accuracy (89.6%) and specificity (85.1%) against Brugada and Vereckei aVR. Performance metrics of the WCT Formula (accuracy 85.8%, sensitivity 84.7%, and specificity 87.1%) and WCT Formula II (accuracy 89.8%, sensitivity 89.6%, and specificity 85.1%) were similar to the VT score (accuracy 84.4%, sensitivity 93.8%, and specificity 74.2%). Paired Model was superior to Brugada in accuracy (89.6% versus 75.2%), specificity (97.0% versus 57.4%), and F1 score (0.89 versus 0.80). Paired Model surpassed Vereckei aVR in accuracy (89.6% versus 65.3%), specificity (97.0% versus 75.2%), and F1 score (0.89 versus 0.74). Paired Model demonstrated similar accuracy (89.6% versus 84.4%), inferior sensitivity (79.3% versus 93.8%), but superior specificity (97.0% versus 74.2%) to the VT score. Solo Model and VT Prediction Model accuracy (82.5% and 77.4%, respectively) was superior to the Vereckei aVR (65.3%) but similar to Brugada (75.2%) and the VT score (84.4%). Automated WCT differentiation algorithms demonstrated favorable diagnostic performance compared with traditional manual ECG interpretation approaches.

Bridging ECG learning with emerging technologies: Advancing clinical excellence

As ECG technology rapidly evolves to improve patient care, accurate ECG interpretation will continue to be foundational for maintaining high clinical standards. Recent studies have exposed significant educational gaps, with many healthcare professionals lacking sufficient training and proficiency. Furthermore, integrating new software and hardware ECG technologies poses challenges about potential knowledge and skill erosion. This underscores the need for clinicians who are adept at integrating clinical expertise with technological proficiency. It also highlights the need for innovative solutions to enhance ECG interpretation among healthcare professionals in this rapidly evolving environment. This work explores the importance of aligning ECG education with technological advancements and proposes how this synergy could advance patient care in the future.

Artificial intelligence–based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram

Abstract Aims Smartwatch electrocardiograms (SW ECGs) have been identified as a non-invasive solution to assess abnormal heart rhythm, especially atrial arrhythmias (AAs) that are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations encountered in clinical cardiology practice. Methods and results A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (respectively, 200 patients per study). Simultaneous ECGs were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95% confidence interval (CI) 85–95%] and 61% (95% CI 44–75%) with a specificity of 95% (95% CI 91–97%) and 97% (95% CI 93–99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis). Conclusion In this study, by including patients from a cardiology department, a DNN-based algorithm applied to an SW ECG provided an accurate diagnosis for AA detection on virtually all tracings, outperforming the SW algorithm.

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: A comparison with cardiologists and emergency medicine specialists

IntroductionGPT-4, GPT-4o and Gemini advanced, which are among the well-known large language models (LLMs), have the capability to recognize and interpret visual data. When the literature is examined, there are a very limited number of studies examining the ECG performance of GPT-4. However, there is no study in the literature examining the success of Gemini and GPT-4o in ECG evaluation. The aim of our study is to evaluate the performance of GPT-4, GPT-4o, and Gemini in ECG evaluation, assess their usability in the medical field, and compare their accuracy rates in ECG interpretation with those of cardiologists and emergency medicine specialists. MethodsThe study was conducted from May 14, 2024, to June 3, 2024. The book “150 ECG Cases” served as a reference, containing two sections: daily routine ECGs and more challenging ECGs. For this study, two emergency medicine specialists selected 20 ECG cases from each section, totaling 40 cases. In the next stage, the questions were evaluated by emergency medicine specialists and cardiologists. In the subsequent phase, a diagnostic question was entered daily into GPT-4, GPT-4o, and Gemini Advanced on separate chat interfaces. In the final phase, the responses provided by cardiologists, emergency medicine specialists, GPT-4, GPT-4o, and Gemini Advanced were statistically evaluated across three categories: routine daily ECGs, more challenging ECGs, and the total number of ECGs. ResultsCardiologists outperformed GPT-4, GPT-4o, and Gemini Advanced in all three groups. Emergency medicine specialists performed better than GPT-4o in routine daily ECG questions and total ECG questions (p = 0.003 and p = 0.042, respectively). When comparing GPT-4o with Gemini Advanced and GPT-4, GPT-4o performed better in total ECG questions (p = 0.027 and p < 0.001, respectively). In routine daily ECG questions, GPT-4o also outperformed Gemini Advanced (p = 0.004). Weak agreement was observed in the responses given by GPT-4 (p < 0.001, Fleiss Kappa = 0.265) and Gemini Advanced (p < 0.001, Fleiss Kappa = 0.347), while moderate agreement was observed in the responses given by GPT-4o (p < 0.001, Fleiss Kappa = 0.514). ConclusionWhile GPT-4o shows promise, especially in more challenging ECG questions, and may have potential as an assistant for ECG evaluation, its performance in routine and overall assessments still lags behind human specialists. The limited accuracy and consistency of GPT-4 and Gemini suggest that their current use in clinical ECG interpretation is risky.

Investigating the Effect of Online Gamification on Electrocardiogram Interpretation and Self-Directed Learning in Nursing Students.

Knowing and interpreting the Electrocardiogram correctly and taking care based on it is one of the most important ways to save the patient's life. Therefore, this study aimed to investigate the effect of online gamification on the ability to interpret Electrocardiograms and Self-Directed Learning in nursing students. The present study was carried out quasi-experimentally on 44 nursing students (22 participants for each group) in the fifth semester in Ilam city in 2023 using a convenience sampling method by four permutations balanced block randomization to control and intervention groups. During four sessions in four weeks, the participants in the intervention group received Electrocardiogram interpretation as training on the Storyline platform and playing games in the Socrative software. The control group received the same content in the form of education from the same lecturer. ECG Interpretation Learning Test, Self-Directed Learning, Mini-Mental Status Examination, and demographic questionnaires were filled out before the intervention (pre-test). Then, Electrocardiogram Interpretation Learning Test and Self-Directed Learning questionnaires were completed two weeks after the end of the intervention (post-test), and the data were analyzed in the standard error of 0.05 using Shapiro-Wilk, independent t-test, paired t-test, chi-square, and multiple linear regression tests in SPSS V.16. The mean and standard deviation of the participants' age was 21.35 ± 1.02, and 59% of the samples were male. Before the intervention, no significant difference was observed between the two groups, but after the intervention, the mean and standard deviation of the ECG interpretation scores in the intervention group increased significantly (14.1±2.94) compared to the control group (8.8±1.36) (P=0.007). Also, Self-Directed Learning in the intervention group (168±10.42) compared to the control group (125±4.3) showed a statistically significant difference (P=0.003). Using online gamification as a new educational method in nursing students improves the ability to interpret Electrocardiograms and Self-Directed Learning. It is suggested that this method should be used to teach lessons that are more useful and important at the hospital.

Interpretation of athlete's ECG in a pediatric population using the International Criteria

Interpretation of athlete's ECG in a pediatric population using the International Criteria

Artificial intelligence driven prehospital ECG interpretation for the reduction of false positive emergent cardiac catheterization lab activations: A retrospective cohort study

Artificial intelligence driven prehospital ECG interpretation for the reduction of false positive emergent cardiac catheterization lab activations: A retrospective cohort study

AI-Powered ECG Interpretation Too Promising to Ignore

AI-Powered ECG Interpretation Too Promising to Ignore