Key result
The K-Nearest Neighbor algorithm with K=3 and Euclidean distance achieved QRS detection rates of 99.89% and 99.81% on the CSE and MIT-BIH Arrhythmia databases, respectively.
The proposed KNN algorithm provides highly accurate and reliable QRS-complex detection in standard ECG databases.
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KNN QRS detection supports ECG algorithm refinement; leaves open prospective clinical validation before practice adoption.
Saini et al. (2012) studied Arrhythmia / ECG analysis (n=173). K-Nearest Neighbor (KNN) algorithm vs. Other published QRS detection algorithms was evaluated on QRS detection rate. The K-Nearest Neighbor algorithm with K=3 and Euclidean distance achieved QRS detection rates of 99.89% and 99.81% on the CSE and MIT-BIH Arrhythmia databases, respectively.
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