ECGruisreductie

ECGruisreductie is Dutch for ECG noise reduction. It refers to methods and algorithms aimed at improving the quality of electrocardiogram signals by reducing non-physiological noise while preserving diagnostically relevant features. The goal is to enable reliable interpretation in clinical settings, during remote monitoring, or in wearable devices where noise is common. The term covers preprocessing steps applied to single-lead or multi-lead ECG data.

Noise sources include electrical interference from power lines, baseline wander caused by respiration, motion artifacts from patient movement, and muscle activity. Electrode impedance changes can also introduce distortion. Effective noise reduction aims to increase signal clarity such that the P wave, QRS complex and T wave are readily identifiable without altering their morphology.

Techniques range from traditional filtering to advanced signal processing. Conventional filtering uses high-pass filters to remove baseline drift, low-pass filters to attenuate high-frequency noise, and notch filters to suppress 50 or 60 Hz interference. Adaptive filters (such as LMS or RLS) exploit reference noise signals. Wavelet denoising, ensemble empirical mode decomposition and independent component analysis are used to separate noise from the ECG.

Applications include hospital telemetry, ambulatory Holter monitoring and consumer wearables. Evaluation focuses on signal-to-noise ratio, preservation of QRS morphology, accuracy of automated ECG feature extraction, and clinician judgment. Trade-offs exist: aggressive noise suppression can distort low-amplitude signals or subtle intervals, while conservative methods may leave residual artifacts. Real-time implementations must balance latency, power consumption, and robustness to non-stationary noise.