Which arrhythmia is represented by the following ECG tracing?

The ECG shows atrial fibrillation, the most common arrhythmia and the one with the fastest atrial rate. Anticoagulants are administered to prevent thrombus formation along the atrial wall.

First step in ECG interpretation: regularity and the missing waveform

The first findings to look for on this tracing are the absence of P waves and an irregular QRS rhythm. In normal sinus rhythm, the electrical impulse generated in the sinoatrial node depolarizes the atria and produces a uniformly shaped P wave before every QRS complex. On this ECG, however, no distinct P waves can be identified; instead, an irregular, quivering baseline is seen in their place. This reflects atria that are not contracting as a unit but are instead fibrillating chaotically at more than 350–600 beats/min. Based on this pathophysiology, the other options can be ruled out: atrial flutter shows clearly identifiable sawtooth F waves, while ventricular flutter and premature ventricular contractions (wide QRS complexes without a preceding P wave) present with a regular QRS pattern or isolated ectopic beats. ^[1]

Clinical significance of atrial fibrillation: the link to stroke and heart failure

Atrial fibrillation should never be dismissed as merely an irregular pulse. Loss of effective atrial contraction causes blood stasis and sharply increases the risk of thrombus formation, particularly in the left atrial appendage. For this reason, atrial fibrillation is one of the leading causes of ischemic stroke. Furthermore, when loss of atrioventricular synchrony reduces cardiac output, chronic heart failure may develop or preexisting heart failure may worsen. In patients with impaired ventricular relaxation—such as those with hypertrophic cardiomyopathy (HCM)—the onset of atrial fibrillation adds hemodynamic burden and becomes a decisive factor in rapid clinical deterioration. ^{[2] [3]}

A silent course: the importance of screening for asymptomatic atrial fibrillation

One reason atrial fibrillation can be difficult to diagnose is that many patients feel no symptoms at all. Such asymptomatic atrial fibrillation is often discovered only after a stroke has already occurred, because diagnosis was delayed. As a result, single-lead ECG monitoring with wearable devices has recently gained attention as a screening tool. Signals obtained in real-world use, however, are vulnerable to motion and muscle artifact, so subtle pathologic features such as loss of P waves or the presence of f waves are easily obscured. To overcome these limitations, deep learning methods that assess signal quality and augment data are being introduced, and they are helping to improve early detection rates in asymptomatic patients. ^{[1] [4]}

Interpretability of machine learning–based diagnosis

When artificial intelligence is brought into clinical practice, it is essential that the algorithm not simply output a result but also explain how it reached that conclusion. Machine learning models for atrial fibrillation detection use interpretation techniques such as SHAP (SHapley Additive exPlanations) to visualize which ECG parameters drove the decision. For example, one can quantify how much features related to RR interval irregularity or absence of P waves contributed to the model's final determination. This interpretability goes beyond simple diagnostic support: it gives clinicians a rational basis for trusting the model's output and integrating it into clinical decision making. ^[2]