ECG signals are vital for cardiac diagnosis, but they often contain unwanted artifacts. Baseline drift causes the signal to wander slowly, while high-frequency noise from electrical interference creates rapid fluctuations. Motion artifacts from patient movement add additional distortions. These artifacts can obscure important cardiac features, making accurate diagnosis challenging.
ECG artifacts can be categorized into three main types. Baseline drift is a low-frequency wandering of the signal, typically between 0.05 and 2 Hz, caused by patient respiration or electrode movement. Power line interference appears as high-frequency noise at 50 or 60 Hz from nearby electrical equipment. Muscle artifacts are high-frequency disturbances between 20 and 200 Hz, resulting from patient movement or muscle tension. Each type requires specific filtering techniques for effective removal.
ECG signal processing involves multiple filtering steps to remove artifacts while preserving cardiac information. High-pass filtering at 0.5 to 1 Hz removes baseline drift caused by respiration and electrode movement. Low-pass filtering at 40 to 100 Hz eliminates high-frequency noise while maintaining the QRS complex shape. Notch filtering at 50 or 60 Hz specifically targets power line interference. The result is a clean ECG signal that clearly shows cardiac features like P waves, QRS complexes, and T waves, enabling accurate diagnosis.
A clean ECG signal exhibits several key characteristics that are essential for accurate cardiac diagnosis. The baseline should be stable without drift, allowing clear identification of cardiac waves. P waves representing atrial depolarization should be clearly visible, followed by sharp QRS complexes indicating ventricular depolarization. T waves showing ventricular repolarization must be distinct and well-formed. The signal should have minimal noise with a signal-to-noise ratio greater than 20 decibels, consistent morphology across beats, and proper amplitude scaling. These features enable healthcare professionals to perform accurate cardiac rhythm analysis and make reliable diagnostic interpretations.
Clean ECG signals are fundamental for various clinical applications. They enable accurate detection of arrhythmias such as atrial fibrillation, ventricular tachycardia, and bradycardia patterns. In myocardial infarction diagnosis, clean signals allow precise identification of ST segment elevations and Q wave abnormalities. Heart rate variability analysis requires artifact-free signals to assess autonomic nervous system function. Modern automated diagnosis systems and remote patient monitoring devices depend entirely on high-quality ECG signals. The comparison shown here demonstrates normal sinus rhythm with regular R-R intervals versus atrial fibrillation with irregular patterns. Proper signal quality is absolutely essential for making accurate clinical decisions and ensuring patient safety.