Application of Artificial Intelligence to the Electrocardiogram

Zachi I. Attia; David M. Harmon; Elijah R. Behr; Paul A. Friedman


Eur Heart J. 2021;42(46):4717-4730. 

In This Article

Abstract and Introduction


Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Graphical Abstract

The application of artificial intelligence to the standard electrocardiogram enables it to diagnose conditions not previously identifiable by an electrocardiogram, or to do so with a greater performance than previously possible. This includes identification of the current rhythm, identification of episodic atrial fibrillation from an ECG acquired during sinus rhythm, the presence of ventricular dysfunction (low ejection fraction), the presence of valvular heart disease, channelopathies (even when electrocardiographically 'concealed'), and the presence of hypertrophic cardiomyopathy.


Despite the fact that the electrocardiogram (ECG) has been in use for over 100 years and is a central tool in clinical medicine, we are only now beginning to unleash its full potential with the application of artificial intelligence (AI). The ECG is the cumulative recording at a distance (the body surface) of the action potentials of millions of individual cardiomyocytes (Figure 1). Traditionally, clinicians have been trained to identify specific features such as ST-segment elevation for acute myocardial infarction, T-wave changes to suggest potassium abnormalities, and other gross deviations to identify specific clinical entities. By their very nature, the magnitude of the changes must be substantial in order to significantly alter a named ECG feature to result in a clinical diagnosis. With the application of convolutional neural networks to an otherwise standard ECG, multiple non-linear potentially interrelated variations can be recognized in an ECG. Thus, neural networks have been used to: identify a person's sex with startling precision [area under the curve (AUC) 0.97]; recognize the presence of left ventricular dysfunction; uncover the presence of silent arrhythmia not present at the time of the recording; as well as identify the presence of non-cardiac conditions such as cirrhosis.[1–3] Many biological phenomena, each of which can leave its imprint on cardiomyocytes electrical function in a unique manner, lead to multiple, subtle, non-linear, subclinical ECG changes. Although ECGs are filtered between 0.05 and 100 Hertz to augment capture of cardiac signals, they likely also are influenced directly and indirectly by nerve activity, myopotentials, as well as anatomic considerations such as cardiac rotation, size, and surrounding body habitus. With large datasets to train a network as to the multiple and varied influences of each of these conditions, powerful diagnostic tools can be developed. In this review, we will offer an overview of machine learning (ML), show specific examples of conditions not previously diagnosed with an ECG that are now recognized, and provide an update of the application and practice and future directions of the AI processed ECG (AI ECG).

Figure 1.

Microelectrodes in a single myocyte (top left) record an action potential (depicted middle panel). Ionic currents and their propagation are sensitive to cardiac and non-cardiac conditions and structural changes. When the aggregated action potentials are recorded at the body surface (top right), the insuring tracing is the electrocardiogram (bottom). ECG, electrocardiogram.

Broadly speaking, AI can be applied in two ways to the ECG.[4] In one, currently performed human skills, such as determining arrhythmias or acute infarction, are performed in an automated manner making those skills massively scalable. The second utilization is to extract information from an ECG beyond which a human can typically perform. In this review, we will focus on the latter.