A window-based time series feature extraction method

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Date

2017-10-01

Author

Katircioglu-Ozturk, Deniz
GÜVENİR, H. ALTAY
Ravens, Ursula
Baykal, Nazife

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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.

Subject Keywords

Time series analysis, Feature extraction, Cardiac action potential, Atrial fibrillation, Electrocardiography, Myocardial infarction

URI

https://hdl.handle.net/11511/31867

Journal

COMPUTERS IN BIOLOGY AND MEDICINE

DOI

https://doi.org/10.1016/j.compbiomed.2017.08.011

Collections

Graduate School of Informatics, Article

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Citation Formats

D. Katircioglu-Ozturk, H. A. GÜVENİR, U. Ravens, and N. Baykal, “A window-based time series feature extraction method,” COMPUTERS IN BIOLOGY AND MEDICINE, pp. 466–486, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31867.