The Application of Computer Techniques to ECG Interpretation

It is over 120 years since Einthoven introduced the electrocardiogram [...]

Inherently explainable deep neural network-based interpretation of electrocardiograms using variational auto-encoders

Background Deep neural networks (DNNs) show excellent performance in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction and prediction of one-year mortality. Despite these promising developments, clinical implementation is severely hampered by the lack of trustworthy techniques to explain the decisions of the algorithm to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods We present a novel approach that is inherently explainable and uses an unsupervised variational auto-encoder (VAE) to learn the underlying factors of variation of the ECG (the FactorECG) in a database with 1.1 million ECG recordings. These factors are subsequently used in a pipeline with common and interpretable statistical methods. As the ECG factors are explainable by generating and visualizing ECGs on both the model- and individual patient-level, the pipeline becomes fully explainable. The performance of the pipeline is compared to a state-of-the-art black box DNN in three tasks: conventional ECG interpretation with 35 diagnostic statements, detection of reduced ejection fraction and prediction of one-year mortality. Results The VAE was able to compress the ECG into 21 generative ECG factors, which are associated with physiologically valid underlying anatomical and (patho)physiological processes. When applying the novel pipeline to the three tasks, the explainable FactorECG pipeline performed similar to state-of-the-art black box DNNs in conventional ECG interpretation (AUROC 0.94 vs 0.96), detection of reduced ejection fraction (AUROC 0.90 vs 0.91) and prediction of one-year mortality (AUROC 0.76 vs 0.75). Contrary to state-of-the-art, our pipeline provided inherent explainability on which morphological ECG features were important for prediction or diagnosis. Conclusion Future studies should employ DNNs that are inherently explainable to facilitate clinical implementation by gaining confidence in artificial intelligence, and more importantly, making it possible to identify biased or inaccurate models.

Beyond STEMI: paramedics' views on how to improve their ability to interpret ECGs

Background: Internationally, the paramedic scope of practice is developing. Bypassing emergency departments in favour of direct access to primary percutaneous coronary intervention laboratories has been limited largely to cases of ST-elevation myocardial infarction and new-onset left bundle branch block, but updates to international guidelines suggest that enhancing paramedics' skills in interpreting electrocardiograms (ECGs) and widening the bypass criteria could be beneficial. Aim: The aim of the study is to explore paramedics' views on ways to improve their ECG interpretation abilities. Method: A two-arm design was used with an online questionnaire (quantitative) and one-to-one interviews (qualitative). The questionnaire results were used to inform the interview guide. Findings: One hundred and eighteen paramedics completed the survey, and 11 took part in interviews. The major themes identified from the template analysis of the interviews were ‘a profession in transition’, ‘lagging professional development’ and ‘supporting the frontline’. Self-directed learning resources before, during and after action were proposed. Conclusion: Paramedicine is evolving in Ireland and practitioners have reported undertaking self-directed learning activities. The resulting heterogeneity in skills such as ECG interpretation, and perceived barriers to education, can cause feelings of vulnerability within the profession. Supporting the frontline by introducing some Group-Orchestrated Self-Directed Learning resources could empower practitioners and contribute to the evolution of prehospital care in Ireland.

ECG Interpretation: Clinical Relevance, Challenges, and Advances

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation.

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, are included. Methods of analysis are considered from single lead to three leads to twelve leads. Some of the influential figures are mentioned while work undertaken locally is used to outline the progress of the technique mirrored in other centres. Applications of artificial intelligence are also considered so that the reader can find out how the field has been constantly evolving over the past 50 years.