Key result
Advanced signal processing methods improve ECG analysis accuracy by reducing noise.
Why the study?
The accuracy of ECG signals can be compromised by various types of noise, requiring effective noise removal techniques to ensure reliable clinical interpretation.
Advanced signal processing and machine learning techniques are crucial for effective noise reduction in ECG signals, thereby enhancing diagnostic reliability in cardiovascular care.
Supports ECG noise reduction in clinical workflows; leaves open prospective validation before practice change.
Electrocardiogram (ECG) and electrocardiography are essential tools for monitoring heart activity and diagnosing cardiovascular diseases. However, the accuracy of ECG signals can be compromised by various types of noise, including environmental factors, patient movement, and electrical interference. Among the most significant noise sources affecting ECG signals are Baseline Wander (BW), Electrode Movements (EM), Motion Artifacts (MA), Additive White Gaussian Noise (AWGN), and Powerline Interference (PLI). This paper provides a detailed review of the challenges posed by these noise sources and explores advanced techniques for minimizing their impact on ECG signals. By employing signal processing methods such as filtering, adaptive algorithms, and wavelet transforms, autoencoders, and generative methods, these techniques aim to improve the reliability and accuracy of ECG data analysis. Enhanced noise reduction enables healthcare professionals to make more precise diagnoses and treatment decisions, ultimately contributing to better patient outcomes in cardiovascular care.
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Midani et al. (2025) conducted a review in Cardiovascular diseases. Noise removal techniques (filtering, adaptive algorithms, wavelet transforms, autoencoders, generative methods) was evaluated on Noise reduction in ECG signals. Advanced signal processing methods, including filtering, adaptive algorithms, and autoencoders, improve the reliability and accuracy of ECG data analysis by reducing various noise sources.
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