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ECG Noise Detection via Clustering Methods by beingwx is a document available to read on EtoBox.

This study presents a novel algorithm for noise detection in electrocardiogram (ECG) signals using agglomerative clustering of morphological features. The method effectively identifies noisy patterns and artifacts with a sensitivity of 88%, specificity of 92%, and accuracy of 91%, demonstrating its robustness across various datasets. The algorithm aims to enhance the accuracy of denoising and can be adapted for signal classification, addressing the challenges posed by noise in biosignal acquisition.

Author

beingwx

Language

EN