ECG Signal Noise Cancellation: A Review

The detection of cardiac disorders relies heavily on electrocardiogram [ECG] readings, but these signals are frequently tainted by a variety of noise sources, including motion-induced disruptions, powerline drift, and muscle artifacts. In order to improve the integrity of ECG recordings, several noise cancellation techniques have been developed. Conventional methods use digital filtering techniques, such as zero phase bandpass filters, to successfully reduce noise while maintaining crucial signal properties. Empirical mode decomposition (EMD)-based adaptive approaches have been developed by advances in signal processing which convert ECG data into intrinsic mode functions, making it easier to remove certain noise components. In order to improve the accuracy of ECG readings, new deep learning techniques or hybrid algorithms have shown exceptional performance in eliminating intricate noise patterns such as baseline, wander, and motion artifacts. All of these developments work together to enhance patient outcomes and diagnostic precision in clinical ambulatory settings by enabling more dependable ECG signal capture.