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- W1982128703 abstract "This Paper describes a clustering approach to be used for incoming data under computational constraints at an early stage of the signal processing chain. The algorithm is evaluated on the MIT-BIH Arrhythmia Database (MIT) and the European STT-Database (EDB) using a pseudo classification method to estimate the beat identification rates. The algorithm allows an extensive computational simplification, still providing reliable pattern recognition results for normal QRS beat types (Se=96.18 %; +P=99.61 % on MIT and Se=98.26 %; +P=99.95 % on EDB) as well as for ventricular ectopic QRS types (Se=97.61 %; +P=99.64 % on MIT and Se=99.07 %; +P=98.93 % on EDB). Besides its performance in terms of pseudo classification, the computational simplicity and few restrictions regarding its applicability render the proposed clustering method an interesting choice for online-clustering applications even apart from ECG processing." @default.
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- W1982128703 date "2013-04-01" @default.
- W1982128703 modified "2023-09-27" @default.
- W1982128703 title "QRS pattern recognition using a simple clustering approach for continuous data" @default.
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- W1982128703 doi "https://doi.org/10.1109/elnano.2013.6552010" @default.
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