Artifact Resolution — Identify and resolve artifacts (e.g., wandering baseline, AC interference)

From the earliest days of electrocardiography, clinicians struggled with the problem of unwanted electrical signals contaminating the cardiac waveform. When Willem Einthoven developed the string galvanometer in 1901, the instrument was so sensitive that any extraneous electrical activity — from the patient's skeletal muscles, nearby power lines, or even the galvanometer's own wiring — would overlay on the cardiac tracing and obscure diagnostic morphology. These unwanted signals became known as artifacts, and from the very inception of the EKG, resolving them has been an essential technical competency. The history of artifact resolution is, in many ways, the history of EKG technology itself — each generation of equipment introduced new solutions while also creating new sources of interference.

Despite over a century of technological advancement, the fundamental challenge remains unchanged: how do we separate the heart's electrical signal from all other electrical activity reaching the electrodes? This question defines the core competency of artifact resolution — the ability to identify the type and source of an artifact and apply the appropriate corrective action, whether that involves adjusting the patient, the electrodes, the equipment, or the environment.

An EKG artifact is any deflection or distortion on the tracing that does not originate from the heart's electrical conduction system. Understanding artifact resolution requires mastery of a few foundational principles that guide both recognition and remediation. The EKG technician must internalize the concept that the electrocardiograph is an exquisitely sensitive voltmeter — it measures potential differences on the order of millivolts (typically 0.5–3.0 mV for the QRS complex), meaning that even minute extraneous electrical signals can produce visible deflections on the tracing. The overarching goal is to maximize the signal-to-noise ratio — the amplitude of the cardiac signal relative to the amplitude of all non-cardiac electrical activity.

Recognizing artifacts begins with pattern recognition. Each artifact type produces a characteristic morphology on the EKG tracing that, with experience, becomes immediately identifiable. The following diagram illustrates the four most commonly encountered artifacts alongside a clean, artifact-free tracing for comparison. Note how each artifact has a distinct visual signature that provides clues about its source and the appropriate corrective action.

Notice that each artifact has a characteristic frequency and amplitude pattern. The wandering baseline in Panel B produces a slow, sinusoidal drift (typically less than 0.5 Hz) that shifts the entire waveform above and below the isoelectric line. In contrast, AC interference in Panel C creates a perfectly regular, fine oscillation at exactly 60 Hz (in North America), giving the tracing a characteristic 'fuzzy' or thickened appearance. Somatic tremor in Panel D is irregular and varies in amplitude, often concentrated in specific leads corresponding to the trembling limb. Motion artifact in Panel E produces sudden, large-amplitude deflections that are clearly non-physiologic in morphology and timing.

Understanding the electrical and physiological mechanisms that generate artifacts is essential for effective troubleshooting. Rather than memorizing corrective steps in isolation, a technician who understands why an artifact occurs can reason through novel situations and apply appropriate interventions even in unfamiliar clinical environments.

Wandering Baseline — The Low-Frequency Drift

Wandering baseline occurs when the isoelectric line shifts vertically over time. The most common cause is respiratory movement, which changes the thoracic impedance and electrode position relative to the heart with each breath cycle. Because normal respiration occurs at approximately 12–20 breaths per minute, this produces a slow oscillation in the 0.2–0.35 Hz range. Other causes include poor electrode adhesion (where the gel dries and impedance fluctuates), patient movement on the exam table, and diaphoresis (excessive sweating), which alters the conductivity of the electrode-skin interface. The clinical danger of wandering baseline is that it can mimic ST-segment elevation or depression, potentially leading to a false diagnosis of myocardial ischemia or injury.

AC (Powerline) Interference — The 60 Hz Signal

Alternating current interference results from electromagnetic fields generated by nearby electrical equipment operating at 60 Hz (50 Hz in European countries). The human body acts as an antenna, and when electrode impedance is elevated or asymmetric between leads, the common-mode rejection circuitry of the EKG machine cannot fully cancel the interference signal. The result is a superimposed sinusoidal waveform with a very consistent period of approximately 16.7 milliseconds (1 ÷ 60 Hz). Sources include power cables running beneath or near the patient, IV pumps, fluorescent lighting ballasts, and electric beds. Notably, the artifact's amplitude is directly proportional to the impedance mismatch between electrodes — which is why proper, uniform skin preparation is the first and most effective intervention.

Somatic Tremor — Skeletal Muscle Noise

Skeletal muscle generates electrical depolarizations in the same millivolt range as the heart. When a patient is shivering (due to cold or anxiety), has a neurological tremor (e.g., Parkinson's disease), or is simply tensing muscles, the resulting electromyographic (EMG) signals are picked up by the surface electrodes. Somatic tremor typically manifests as irregular, high-frequency deflections in the 20–300 Hz range. Unlike AC interference, which is perfectly regular, somatic tremor varies in amplitude and frequency, giving the tracing a rough, jagged appearance. This artifact most frequently appears in limb leads because limb muscles are large and close to the electrodes.

Motion Artifact — Electrode Displacement

Motion artifact occurs when an electrode is physically displaced relative to the skin, causing abrupt changes in the half-cell potential at the electrode-skin interface. This generates large, sharp deflections that are often easily distinguished from cardiac waveforms by their non-physiologic morphology and lack of regularity. Common causes include patient movement (turning, coughing, adjusting position), cable tension pulling on electrodes, and electrode detachment. In telemetry and ambulatory (Holter) monitoring, motion artifact is particularly problematic because the patient is moving continuously.

Effective artifact resolution requires a systematic approach. The following diagram presents a decision-tree flowchart that guides the technician from initial artifact identification through root cause analysis to the appropriate corrective action. This troubleshooting algorithm should become second nature for every EKG technician.

The following scenario demonstrates a systematic approach to identifying and resolving an artifact encountered during a standard 12-lead EKG acquisition. This mirrors a real-world clinical situation and applies the troubleshooting algorithm presented in Section 5.

One of the most critical skills for an EKG technician is distinguishing artifacts from genuine cardiac pathology. Several artifacts can closely mimic dangerous arrhythmias or ischemic changes, and misidentification in either direction carries significant clinical consequences. The following table compares commonly confused artifact-pathology pairs and identifies the distinguishing features that allow accurate differentiation.

While proper electrode technique and patient preparation should always be the first line of artifact resolution, modern EKG machines provide digital filters that can further refine signal quality. Understanding how these filters work — and their limitations — is increasingly important as EKG technology advances. Digital filters selectively attenuate signals within specific frequency ranges, but because cardiac waveform components span a wide frequency spectrum (0.05–150 Hz for diagnostic-quality EKGs), any filter that removes artifact frequencies risks simultaneously distorting true cardiac data.

As you progress to advanced cardiac monitoring, Holter analysis, and stress testing, you will encounter more sophisticated signal processing techniques, including wavelet denoising and template matching algorithms. These tools are powerful, but the foundational principle remains: no digital algorithm can substitute for a properly prepared patient and correctly applied electrodes. The EKG technician's role as the primary defense against artifacts will remain essential even as technology continues to evolve.