An artifact is a pattern in a signal that looks like a feature but isn’t real — typically caused by something other than the phenomenon being measured. A spike on an ECG trace caused by the patient briefly touching the lead wire is an artifact: it looks like a heartbeat but isn’t one. The reading reflects an external interference, not the underlying cardiac signal.
The distinction worth keeping clear is artifact vs. noise. Noise obscures real features — it adds randomness on top of the signal, making the real pattern harder to see. An artifact mimics a feature — it creates a pattern in the data that looks real but doesn’t reflect what we wanted to measure. In the literature the terms are often used interchangeably, but technically:
- Noise hides features. Smoothing reduces it.
- Artifacts mimic features. Smoothing can hide them but doesn’t actually fix the underlying mistake.
Common artifact sources:
- Electrode movement in an ECG or EEG recording — a touch, a stretch, a lead becoming loose — produces brief spikes or step changes that look like real signal but aren’t.
- Power-line hum at 50 or 60 Hz (depending on the country) appears as a periodic ripple, easily mistaken for a real periodic signal.
- Saturation when a signal exceeds the sensor’s dynamic range produces flat plateaus at the maximum or minimum value — clearly artifacts because the real signal is varying behind them.
- Quantization when the sensor’s resolution is too coarse for the signal’s amplitude — the recording looks stepped rather than smooth.
- Aliasing when the sampling rate is too low for the signal’s frequency content — real high-frequency content gets mirrored into the low-frequency band as a fake pattern.
The right response to artifacts is usually different from the right response to noise. For noise: smooth the signal (e.g., Moving-average filter). For artifacts: identify the cause and either fix it upstream (better electrode contact, properly shielded cables) or detect and reject the affected segments. A Moving-average filter applied to an artifact might smooth it into something less obvious, but the data underneath is still wrong — the model trained on it will learn the wrong things.
In machine-learning pipelines, knowing the difference matters. A robust preprocessing pipeline detects common artifacts (saturation plateaus, sudden steps, missing-data segments) and either flags them or removes the affected windows entirely. Treating artifacts as if they were noise — running them through a smoothing filter and hoping — leads to models that work in the lab and fail in the field.