Electrocardiogram Interpretation Research Articles

Review on Cardiac Arrhythmias with ECG Interpretation on Different Myocardial Regions

Electrocardiogram (ECG) is one of the major diagnostics tools for Cardiac Arrhythmias (CA). ECG interpretation is more important to provide specific treatment of different arrhythmic myocardial regions. In this article the aim is to focus on detailed normal and abnormal ECG interpretation with different myocardial regions. Contraction and Relaxation Process (CRP) during cardiac cycle generates complex ECG with addition of each conduction node electrical vector activity in cardiac conduction system. Epinephrine stimulates Beta1 adrenergic receptors to produce more G stimulation proteins and thus leads to increase calcium level in the cell that provides frequent production of Action Potential (AP) like sinus tachycardia, Early Afterdepolarization (EAD) and Delayed Afterdepolarization (EAD). Myocardial scar regions, AP cannot move because of blockage, in turns moving rapidly around the dead area in a circular way to lead reentrant arrhythmias. On the other hand of respiration cycle, vagus outflow fluctuates (inspiration) the Sinus Node (SN) to increase heart rate (R-R distance will decrease, more P wave and QRS complex will be produced) and to decrease heart rate during expiration (R-R distance will be more). Finally conclude that different myocardial dysfunction regions are the major cause of multi-classified cardiac arrhythmias.

Abstract P276: Assessment of the Competence in Electrocardiographic Interpretation Among Arabic Resident Doctors at the Emergency Medicine and Internal Medicine Departments: A Multi-Center Online Cross-Sectional Study

Background: The ability to interpret electrocardiograms (ECG) is crucial for accurate diagnosis and treatment of the most relevant cardiac abnormalities. This study aims to assess the electrocardiographic interpretation abilities of resident doctors at internal medicine and emergency medicine departments in eight Arabic countries, emphasizing the most relevant cardiac abnormalities. Methods: An online cross-sectional study was conducted between 7 th October 2022 and 21 st October 2022 in 8 Arabic countries (Syria, Jordan, Iraq, Qatar, Yemen, Egypt, Sudan, and Algeria) to evaluate the ECG interpretation skills of resident doctors at internal medicine and emergency medicine departments. The questionnaire consisted of two main sections; the first section included sociodemographic information, while the second section contained twelve clinical case questions of the most severe cardiac abnormalities with their ECG recordings. We used the STATA and Excel Microsoft programs to conduct the analysis. Results: Out of 2509 responses, 630 were eligible for the data analysis (Response rate=25.1%). The average age was 26.85±1.7, and more than half of the participants were males (52.4 %). Internal medicine residents were (n=530, 84.1%), whereas emergency medicine residents were (n=100, 15.9%). Almost participants were in their first and second years of residency (79.8%). Only 36.2% of the inquired resident doctors have attended an ECG course. The majority of participants 85.6% recognized the ECG waves order correctly. 50.5% of the participants scored above 7.5/10 on the ECG interpretation scale. The proportions of participants who properly diagnosed atrial fibrillation, third-degree heart block, and atrial tachycardia were 71.1%, 76.7%, and 56.6%, respectively. No statistically significant difference was defined between the internal and emergency medicine residents regarding of knowledge toward ECG interpretation (P value=0.42). However, there was a significant correlation between ECG interpretation and medical residency year (P value<0.001); which, the fourth-year resident doctors have the highest score (mean=9.24, SD =1.6). As well, participants in the third and second years of medical postgraduate residency have a probability of adequate knowledge of ECG interpretation more than participants in the first year of residency (OR=2.1, 95%CI: 1.57-4, P-value=0.001), (OR=1.88, 95%CI: 1.26-2.7, P-value=0.002) respectively. Conclusion: According to the findings of our research, resident doctors in departments of internal medicine and emergency medicine in Arabic nations have adequate ECG interpretation abilities; nevertheless, additional development is required to avoid misconceptions about critical cardiac conditions.

To Learn, Try Teaching

To Learn, Try Teaching

The International Criteria for Electrocardiogram Interpretation in Athletes: Common Pitfalls and Future Directions.

Preparticipation cardiovascular screening (PPCS) in young athletes is performed to detect conditions associated with sudden cardiac death. Many medical societies and sports governing bodies support the addition of a 12-lead electrocardiogram (ECG) to the history and physical to improve PPCS sensitivity. The current standard for ECG interpretation in athletes, the International Criteria, was developed to distinguish physiologic from pathologic ECG findings in athletes. Although application of the International Criteria has reduced the PPCS false-positive rate, interpretative challenges and potential areas of improvement remain. This review provides an overview of common pitfalls and future directions for ECG interpretation in athletes.

What training should psychiatrists have to interpret six- and 12-lead electrocardiograms?

To monitor for drug-related cardiac arrhythmias, psychiatrists regularly perform and interpret 12-lead (12L) and, increasingly often, six-lead (6L) electrocardiograms (ECGs). It is not known how training on this complex skill is updated or how well psychiatrists can interpret relevant arrhythmias on either device.We conducted an online survey and ECG interpretation test of cardiac rhythms relevant to psychiatrists.A total of 183 prescribers took part; 75% did not regularly update their ECG interpretation skills, and only 22% felt confident in interpreting ECGs. Most participants were able to recognise normal ECGs. For both 6L and 12L ECGs, the majority of participants were able to recognise abnormal ECGs, but fewer than 50% were able to correctly identify relevant arrhythmias (complete heart block and long QTc). A small number prescribed in the presence of potentially fatal arrhythmias. These findings suggest a need for mandatory ECG interpretation training to improve safe prescribing practice.

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning

BackgroundPulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes? Methods and ResultsAdults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012–2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients’ 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater. ConclusionsA deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.

Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification

Background and ObjectiveDeep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis. MethodsWe have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925). ResultsThe DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. ConclusionsThis is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.

Evaluating a Blended Learning Model for Medical Student ECG Teaching.

The ability to interpret a 12-lead electrocardiogram (ECG) is an essential skill in inpatient and outpatient settings. In medical school, this skill is generally taught during the Internal Medicine clerkship. Blended learning is a pedagogical tool that combines different modes of information delivery, models of teaching, and learning styles combining face-to-face learning sessions with online learning. The objectives of this study were to develop a curriculum using a blended educational model including lecture, focused educational videos, flipped classroom, and team-based learning to teach a systematic approach to ECG interpretation and enhance the ability of students to identify common and life-threatening electrocardiographic abnormalities. Between 2016 and 2019, 349 medical students from the University of Colorado School of Medicine received the blended learning curriculum, which included an introductory lecture followed by five 30-minute sessions. These sessions encompassed preclass videos and team-based learning in a flipped-classroom design covering critical concepts in electrocardiography. A sample of 64 students completed a survey evaluating confidence in ECG interpretation skills before and after the curriculum. All of the students completed a 17-item pretest and posttest. The new curriculum improved learner confidence in ECG interpretation (Wilcoxon signed rank-sum test, P < 0.001). Postcurriculum test scores showed statistically significant improvement in all of the diagnoses tested (paired Student t test, P < 0.01), the most significant gains occurring in the life-threatening tracings of ventricular fibrillation and in ventricular tachycardia. Using a blended learning model with multiple educational modalities resulted in significant improvement in learners' performance and confidence in ECG interpretation.

Surfing the clinical trials of ECG teaching to medical students: A systematic review.

Interpreting an electrocardiogram (ECG) is crucial for every physician. The physician's competency in ECG interpretation needs to be improved at any stage of medical education. The aim of the present study was to review the currently published clinical trials of ECG teaching to medical students and provide suggestions for future works. On May 1, 2022, PubMed, Scopus, Web of Science, Google Scholar, and ERIC were searched to retrieve relevant articles on clinical trials of ECG teaching to medical students. The quality of the included studies was assessed utilizing the Buckley et al. criteria. The screening, data extraction, and quality appraisal processes were duplicated independently. In case of disagreements, consultation with a third author was put forth. In total, 861 citations were found in the databases. After screening abstracts and full texts, 23 studies were deemed eligible. The majority of the studies were of good quality. Peer teaching (7 studies), self-directed learning (6 studies), web-based learning (10 studies), and various assessment modalities (3 studies) comprised the key themes of the studies. Various methods of ECG teaching were encountered in the reviewed studies. Future studies in ECG training should focus on novel and creative teaching methods, the extent to which self-directed learning can be effective, the utility of peer teaching, and the implications of computer-assisted ECG interpretation (e.g., artificial intelligence) for medical students. Long-term knowledge retention assessment studies based on different approaches integrated with clinical outcomes could be beneficial in determining the most efficient modalities.

Nurses’ knowledge about electrocardiogram interpretation: A cross-sectional study

Objective: To determine the level of nurses’ knowledge about electrocardiograms. Methodology: This cross-sectional design study was conducted from November 2022 and March 2023. It included 100 nurses who were working in Al-Fayhaa Hospital and the Basrah Teaching Hospital. The competence of nurses was assessed using a standardized questionnaire that was developed and distributed to the respondents involved. The data collected were analyzed using SPSS version 26. Results: Nearly to half of nurses had fair understanding regarding ECG interpretation, while one third of study participants had good knowledge. Conclusion: Most nurses have a solid understanding of ECG interpretation.

ECG Tutor: desenvolvimento e avaliação de um sistema tutor inteligente gamificado para ensino de eletrocardiograma

Abstract: Introduction: New methodological approaches have been suggested in an attempt to improve the acquisition of skills in electrocardiogram (ECG) interpretation, known to be complex and difficult to assimilate. Considering the expansion of active methodologies and the encouragement of technological innovations for medical education, the use of internet technologies (e-learning) has gained strength, highlighting the use of artificial intelligence platforms, such as intelligent tutoring systems (ITS), for their capacity to promote adaptive instruction. Researchers have used gamification (game design techniques) in conjunction with ITS, reporting greater engagement. Objective: Seeking to improve ECG learning performance among medical students through the development of new methodologies and considering the benefits of ITS and the use of gamification in medical education, the present study aimed to explore the interest and motivation of medical students in the use joint use of these technologies to study ECG. Method: This is an exploratory, quantitative study, in which graphic design prototypes of a gamified ECG instructor ITS were created by teachers of the medical course and evaluated by medical students from a public university and another private university after improvement into an interactive format, with the contribution of an interdisciplinary team. Result: At first, 53 undergraduate students from the 5th to the 11th semesters of the course participated in the study. That number was reduced to 14 in the second stage. The analysis showed approval of aspects related to perceived easy-to-use format, perceived usefulness, attitude towards use and intention to use. On the other hand, an urgent need to adapt to mobile devices and improve gamification and aesthetic elements was observed. Conclusion: The obtained results allowed us to conclude that medical students intend to use the ECG Tutor in the future, implying the need to develop a computational solution for the system and measure its impact on student learning.

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.

Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease

Artificial intelligence (AI) is that encompasses machine learning (ML) combined with human intelligence had begun to reform medical practices into a new dimension. Advancements and developments of AI molds improved diagnostics in the field of cardiology. Electrocardiogram (ECG) is a simple and cost-effective tool to identify cardiac disorder and which is its reason for being into practice till date. Increasing the population of ECG big data annually requires automatic analysis and immediate interpretation for improved diagnosis. Modern AI techniques like deep learning (DL)-based convolutional neural networks (CNNs) provide an improved way of cardiac disease management and diagnosis. This review throws a light over application of AI in ECG analysis and its necessity. Rich sets of clinical ECG data curated carefully as private and public access developed for various cardiac and extra-cardiac diseases management. Rather than human ECG interpretation, AI can move modern medicine toward more personalized patient care. The intention of this review article is to assess clinical and research possibilities, gaps, and jeopardies involved in cardiac anomalies detection using ECG measurement.

Electrocardiogram interpretation in children: The key role of age, gender, and ethnicity.

Electrocardiogram interpretation in children: The key role of age, gender, and ethnicity.

Does the Electrocardiogram Machine Interpretation Affect the Ability to Accurately Diagnose ST-Elevation Myocardial Infarction by Emergency Physicians?

An ST-elevation myocardial infarction (STEMI) can portend significant morbidity and mortality to the patient and therefore must be rapidly diagnosed by an emergency medicine (EM) physician. The primary aim of this study is to determine whether EM physicians are more or less likely to accurately diagnose STEMI on an electrocardiogram (ECG) if they are blinded to the ECG machine interpretation as opposed to if they are provided the ECG machine interpretation. We performed a retrospective chart review of adult patients over 18 years of age admitted to our large, urban tertiary care center with a diagnosis of STEMI from January 1, 2016, to December 31, 2017. From these patients' charts, we selected 31 ECGs to create a quiz that was presented twice to a group of emergency physicians. The first quiz contained the 31 ECGs without the computer interpretations revealed. The second quiz, presented to the same physicians 2 weeks later, contained the same set of ECGs with the computer interpretations revealed. Physicians were asked "Based on the ECG above, is there a blocked coronary artery present causing a STEMI?" Twenty-five EM physicians completed two 31-question ECG quizzes for a total of 1550 ECG interpretations. On the first quiz with computer interpretations blinded, the overall sensitivity in identifying a "true STEMI" was 67.2% with an overall accuracy of 65.6%. On the second quiz in which the ECG machine interpretation was revealed, the overall sensitivity was 66.4% with an accuracy of 65.8 % in correctly identifying a STEMI. The differences in sensitivity and accuracy were not statistically significant. This study demonstrated no significant difference in physicians blinded versus those unblinded to computer interpretations of possible STEMI.

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.

Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.

A novel convolutional neural network structure for differential diagnosis of wide QRS complex tachycardia

Background and objectivesCardiac arrhythmias are a significant cause of morbidity and mortality in patients with cardiovascular disease. Accurate rhythm diagnosis is critical in patients presenting with wide QRS complex tachycardia (WCT). Real-time visual interpretation of electrocardiograms (ECG) of complex arrhythmias is difficult and requires expertise. We designed a convolutional neural network (CNN) that could accurately classify WCT into those that are ventricular in origin (ventricular tachycardia (VT)) or supraventricular tachycardia with aberrancy (SVT). MethodsA total of 3065 patients with wide complex ECGs were screened (415 with VT and 2650 with SVT). A CNN model was designed through a Neural Architecture Search (NAS) method. This CNN consisted of a stem convolution layer and five cells, each cell containing separable-convolution and dilated-separable-convolution layers. ResultsUsing 5-fold cross-validation and executing algorithm for five independent runs (with five different seeds), the proposed CNN model achieved a detection accuracy of 87.5 ± 0.0025 and 91.7 %±0.0004 for VT and SVT, respectively. The total sensitivity, specificity, positive predictive value, negative predictive value and F1-score of the CNN model were 88.50 %, 88.50 %, 88.54 %, 88.54 %, and 88.49 %, respectively. ConclusionsIn a cohort of patients presenting with a WCT, our CNN model achieved an accuracy of 87.5% and 91.7% to correctly diagnose VT and SVT, respectively. This model has the potential of being used in real-time settings and to assist physicians with interpretation and decision making.

Clinical relevance of the single-lead ECG of the Apple Watch

Abstract Introduction The Apple Watch (AW) is one of the first commercially available wearable with built-in electrocardiogram (ECG) electrodes to perform a single-lead ECG to detect atrial fibrillation (AF). The AW ECG application showed to have a high rate of unclassifiable notifications. The interpretation of the single-lead ECG by a physician can possibly reduce the total of unclassifiable notifications and contribute to the overall accuracy to detect AF. Methods This was a prospective, non-randomized, single center observational study to evaluate the accuracy and interrater agreement of the single-lead ECG of the AW. Patients scheduled for cardioversion for atrial fibrillation were asked for participation. The AW series 6 was used with WatchiOS 7.2 or 7.3 and ECG algorithm version 2.0. A single-lead ECG was obtained pre-conversion and, when cardioversion was successful, post-conversion. All single-lead ECGs and 12-lead ECGs were adjudicated by two physicians, agreement was reached by consensus. The AW single-lead ECG notification was compared to the physicians “gold-standard” interpretation of the 12-lead ECG. Sensitivity and specificity of the AW single-lead ECG and Kappa coefficient were calculated. Results In total, 74 patients were included. Mean age was 67.1±12.3 years and 20.3% were female. In total 65 AF and 64 sinus rhythm (SR) single-lead ECG measurements were obtained. Of these measurements, 27.9% (36/129) showed an unclassifiable notification. The AW ECG notification showed a sensitivity of 93.5% to detect AF and a specificity of 100% to detect SR (K= 0.94). After adjudication of the unclassifiable notifications by two independent physicians 1.6% (2/129) of the measurements remained unclassifiable. When including the by the physicians adjudicated unclassifiable recordings the sensitivity to diagnose AF was 89.2% and the specificity to detect SR was 93.8% (K=0.83). The adjudication of all single-lead ECGs by the two physicians showed a sensitivity of 90.6% to detect AF and specificity of 95.2% to detect SR (K= 0.83) (Table 1). All SR and AF notification by the AW were similarly adjudicated by the two physicians. The kappa coefficient for interrater agreement between both physicians was 0.69 for all single-lead ECGs and 0.58 for unclassifiable notifications specific. Conclusion Unclassifiable notifications of the AW can be reduced by physicians interpretation of the single-lead ECG, however the interrater agreement is only moderate. The physicians interpretation of the single-lead ECGs did not increase the accuracy to detect AF and the clinical relevance of the single-lead ECG, in addition to the AW notification, should therefore be questioned. Funding Acknowledgement Type of funding sources: None.

Impact of diagnostic checklists on the interpretation of normal and abnormal electrocardiograms.

Checklists that aim to support clinicians' diagnostic reasoning processes are often recommended to prevent diagnostic errors. Evidence on checklist effectiveness is mixed and seems to depend on checklist type, case difficulty, and participants' expertise. Existing studies primarily use abnormal cases, leaving it unclear how the diagnosis of normal cases is affected by checklist use. We investigated how content-specific and debiasing checklists impacted performance for normal and abnormal cases in electrocardiogram (ECG) diagnosis. In this randomized experiment, 42 first year general practice residents interpreted normal, simple abnormal, and complex abnormal ECGs without a checklist. One week later, they were randomly assigned to diagnose the ECGs again with either a debiasing orcontent-specific checklist. We measured residents' diagnostic accuracy, confidence, patient management, and time taken to diagnose. Additionally, confidence-accuracy calibration was assessed. Accuracy, confidence, and patient management were not significantly affected by checklist use. Time to diagnose decreased with a checklist (M=147s (77)) compared to without a checklist (M=189s (80), Z=-3.10, p=0.002). Additionally, residents' calibration improved when using a checklist (phase 1: R2=0.14, phase 2: R2=0.40). In both normal and abnormal cases, checklist use improved confidence-accuracy calibration, though accuracy and confidence were not significantly affected. Time to diagnose was reduced. Future research should evaluate this effect in more experienced GPs. Checklists appear promising for reducing overconfidence without negatively impacting normal or simple ECGs. Reducing overconfidence has the potential to improve diagnostic performance in the long term.

The Virtual Cardiology Rotation: Situated Cognition and the Signature Pedagogy in Medicine

Background: Corona Virus 2019 (COVID-19) challenged the delivery of medical education and training, especially to the 4th year medical students. Medicine's signature pedagogy, the ward rounds in the hospital, was put on hold in compliance with safety precautions during this pandemic and the Commission on Higher Education’s (CHED) suspension of face-to-face classes in all levels. How the University of Santo Tomas Faculty of Medicine &amp; Surgery (UST FMS) Cardiology Rotation was delivered despite these restrictions is described. Using Google Classroom, Blackboard Collaborate, and Zoom, a 7-day online Cardiology Clerkship rotation was delivered to UST FMS 4th year students (clerks) using the synchronous and asynchronous mode of delivery. It consisted of the following: 1) Cardiovascular (CV) Ward Rotation with 3 cases using Google Classroom's adaptive release to create a vicarious experience of taking care of a patient and doing all the tasks that a clerk is supposed to do; 2) Virtual Ward Rounds by a consultant; 3) Outpatient Department (OPD) / Ambulatory Care Services (ACS) case discussions with a consultant and a 4) Heart Station Rotation for training in electrocardiogram (ECG) interpretation. Conclusion: The advances in technology and software provided just-in-time resources that made possible the creative delivery of Medicine's signature pedagogy. Key words: virtual cardiology rotation, clerkship, signature pedagogy