A window-based time series feature extraction method

Download

index.pdf

Date

2017-10-01

Author

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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

382
views

917
downloads

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

Suggestions

OpenMETU
Core

A Window-based characterization method for biophysical time series Katırcıoğlu, Deniz; Baykal, Nazife; Department of Medical Informatics (2017) In thesis, we propose a robust similarity score-based time series characterization method, termed as Window-based Time series Characterization (WTC). Specifically, WTC generates domain-interpretable results and involves remarkably low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, we apply WTC to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available ...
A temporal neuro-fuzzy approach for time-series analysis Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003) The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may ...
A Radar Target Recognition Method with MUSIC Algorithm: Application to Aircraft Targets with Measured Scattered Data Secmen, M.; Turhan-Sayan, G.; Sayan, Gönül (2008-05-30) This paper demonstrates the usefulness of an ultra wideband target recognition method in the case of realistic and complicated target geometries at resonance region. The method utilizes the MUSIC algorithm to extract the natural resonance-related scattering features of targets. The resulting features give the power distribution maps of targets. These maps are called as fused MUSIC spectrum matrices and used as the main target recognition feature in the method. The fusion process is needed to reduce the aspe...
A Web Search Enhanced Feature Extraction Method for Aspect-Based Sentiment Analysis for Turkish Informal Texts Kama, Batuhan; ÖZTÜRK, MURAT; Karagöz, Pınar; Toroslu, İsmail Hakkı; Ozay, Ozcan (2016-09-08) In this article, a new unsupervised feature extraction method for aspect-based sentiment analysis is proposed. This method improves the performance of frequency based feature extraction by using an online search engine. Although frequency based feature extraction methods produce good precision and recall values on formal texts, they are not very successful on informal texts. Our proposed algorithm takes the features of items suggested by frequency based feature extraction methods, then, eliminates the featu...
A novel method for electromagnetic target classification using the music algorithm: Applied to small-scale aircraft targets Secmen, Mustafa; Sayan, Gönül (2006-01-01) This paper introduces a novel target classification method based on the extraction of target features by using natural response related late-time electromagnetic scattered field data. In the feature extraction stage, the use of multiple signal classification (MUSIC) algorithm together with a simple but effective feature fusion approach leads to a significant reduction in the sensitivity of classification accuracy to both aspect angle variations and the signal-to-noise ratio (SNR) levels of the data. Another...

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.