QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases
نویسندگان
چکیده
منابع مشابه
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases
The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pas...
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A robust and numerically-efficient method based on a prior knowledge of the ECG event durations is presented. Two optimized event-related moving average filters followed by event-related threshold have been developed to detect QRS complexes. The novelty rises from using window sizes related to the QRS and heartbeat durations. Interestingly, the QRS detector obtained a sensitivity of 99.29% and ...
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The current state-of-the-art in automatic QRS detection methods show high robustness and almost negligible error rates. In return, the methods are usually based on machine-learning approaches that require sufficient computational resources. However, simple-fast methods can also achieve high detection rates. There is a need to develop numerically efficient algorithms to accommodate the new trend...
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ژورنال
عنوان ژورنال: Journal of Advanced Research
سال: 2013
ISSN: 2090-1232
DOI: 10.1016/j.jare.2012.05.007