Prediction of arrhythmia using multivariate time series data다변량 시계열 자료를 이용한 부정맥 예측
- Other Titles
- 다변량 시계열 자료를 이용한 부정맥 예측
- Authors
- 이민혜; 노호석
- Issue Date
- Oct-2019
- Publisher
- 한국통계학회
- Keywords
- 부정맥예측; 다변량 시계열; 최근접이웃방법; 시계열 간 거리함수; 심실빈맥; arrhythmia prediction; multivariate time series; 1-nearest neighbor; time series distance function; ventricular tachycardia
- Citation
- 응용통계연구, v.32, no.5, pp 671 - 681
- Pages
- 11
- Journal Title
- 응용통계연구
- Volume
- 32
- Number
- 5
- Start Page
- 671
- End Page
- 681
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1754
- DOI
- 10.5351/KJAS.2019.32.5.671
- ISSN
- 1225-066X
2383-5818
- 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.
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