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http://dx.doi.org/10.5351/KJAS.2019.32.5.671

Prediction of arrhythmia using multivariate time series data  

Lee, Minhai (Department of Statistics, Sookmyung Women's University)
Noh, Hohsuk (Department of Statistics, The Research Institute of Natural Sciences, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.5, 2019 , pp. 671-681 More about this Journal
Abstract
Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.
Keywords
arrhythmia prediction; multivariate time series; 1-nearest neighbor; time series distance function; ventricular tachycardia;
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