A Window-based characterization method for biophysical time series

Date

2017

Author

Katırcıoğlu, Deniz

Metadata

Show full item record

Item Usage Stats

148
views

0
downloads

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 electrocardiogram (ECG) dataset. We, then, compare WTC with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the former), in terms of predictive accuracy and computational complexity. 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 characterization capability, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets.

Subject Keywords

Atrial fibrillation., Electrocardiography., Time-series analysis., Heart

URI

http://etd.lib.metu.edu.tr/upload/12621008/index.pdf
https://hdl.handle.net/11511/26464

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

A window-based time series feature extraction method Katircioglu-Ozturk, Deniz; GÜVENİR, H. ALTAY; Ravens, Ursula; Baykal, Nazife (2017-10-01) 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 publi...
A Computational approach to detect inhomogeneities in time series data Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci; Department of Statistics (2017) Detection of possible inhomogeneity within a series is an important problem in time series data. There are many sources from which inhomogeneity can be originated such as mean shift, variance and trend change, gradual change, or sudden decrease or increase in time series. Since time series has many application areas, the detection of changepoints should be investigated before conducting any analysis. Available methods have certain drawbacks that may lead to unreliable inferences. These include the need of i...
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 FAST IMAGE-RECONSTRUCTION ALGORITHM FOR ELECTRICAL-IMPEDANCE TOMOGRAPHY Kuzuoğlu, Mustafa; Leblebicioğlu, Mehmet Kemal (IOP Publishing, 1994-05-01) In this paper, we propose a fast algorithm for the reconstruction of the conductivity perturbation DELTAsigma about a known conductivity variation sigma0. The method is based on the minimization of a quadratic functional subject to linear constraints, where the existence of a unique solution is guaranteed. The algorithm developed for this purpose is iterative and each iteration is composed of a simple matrix multiplication. The validity of this method is illustrated with several examples.
A MODEL FOR TONAL CONTEXT TIME COURSE CALCULATION FROM ACOUSTICAL INPUT IZMIRLI, O; BILGEN, S (Informa UK Limited, 1996-09-01) This paper presents a two‐stage model that calculates tonal context as a continuous function in time. The model is capable of processing acoustical polyphonic inputs. The first stage is a simple note recogniser that produces pitch‐class note output corresponding to the acoustical input. A bank of leaky integrators constitute the second stage. The integration rates and saturation limits of these integrators vary with each musical event according to the relations among input notes and the candidates of tonal ...

Citation Formats

D. Katırcıoğlu, “A Window-based characterization method for biophysical time series,” Ph.D. - Doctoral Program, Middle East Technical University, 2017.