A window-based time series feature extraction method


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Katircioglu-Ozturk D., GÜVENİR H. A. , Ravens U., BAYKAL N.

COMPUTERS IN BIOLOGY AND MEDICINE, vol.89, pp.466-486, 2017 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 89
  • Publication Date: 2017
  • Doi Number: 10.1016/j.compbiomed.2017.08.011
  • Journal Name: COMPUTERS IN BIOLOGY AND MEDICINE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.466-486
  • Keywords: Time series analysis, Feature extraction, Cardiac action potential, Atrial fibrillation, Electrocardiography, Myocardial infarction, ACUTE MYOCARDIAL-INFARCTION, ION CHANNELS, ALGORITHM, ECG, SHAPE

Abstract

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets.