Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Feature extraction of hidden oscillation in ECG data via multiple-FOD method
Date
2019-10-30
Author
Purutçuoğlu Gazi, Vilda
Erkuş, Ekin Can
Metadata
Show full item record
Item Usage Stats
254
views
0
downloads
Cite This
Fourier transform (FT) is a non-parametric method which can be used to convert the time domain data into the frequency domain and can be used to find the periodicity of oscillations in time series datasets. In order to detect periodic-like outliers in time series data, a novel and promising method, named as the outlier detection via Fourier transform (FOD), has been developed. From our previous studies, it has been shown that FOD outperforms most of the commonly used approaches for the detection of outliers when the outliers have periodicity with low fold changes or high sample sizes. Recently, the multiple oscillation and hidden periodic-like pattern cases for time series data have been investigated and found that the multiple application of FOD, shortly multiple-FOD, can also be a successful method in the detection of such patterns. These empirical results are based on real electrocardiogram (ECG) data where the discrimination of disorders can be helpful for the diagnosis of certain heart diseases in advance. Hereby, in this study, we evaluate the performance of multiple-FOD in different types of simulated datasets which have distinct sample sizes, percentage of outliers and distinct hidden patterns.
Subject Keywords
Outlier detection
,
Fourier transform
,
Time series data
,
Electrocardiogram
URI
https://hdl.handle.net/11511/87335
DOI
https://doi.org/10.1007/978-3-030-36178-5_5
Conference Name
The International Conference on Artificial Intelligence and Applied Mathematics in Engineering ICAIAME 2019
Collections
Graduate School of Natural and Applied Sciences, Conference / Seminar
Suggestions
OpenMETU
Core
Detection of hidden patterns in time series data via multiple-time FOD method
Erkuş, Ekin Can; Purutçuoğlu Gazi, Vilda (2019-06-23)
The periodicity in time series data can be detected by several frequency domains’ methods, especially, by the Fourier transform (FT). FT is a non-parametric method to convert the time domain data into the frequency domain and it is used in many engineering and data science applications. Recently this method which has been used to detect outliers in time series observations where the data may also include some systematic patterns is called “outlier detection via Fourier transform” (FOD). From our previous ...
Efficient analysis of phased arrays of microstrip patches using a hybrid generalized forward backward method/Green's function technique with a DFT based acceleration algorithm
Bakir, Onur; Aydın Çivi, Hatice Özlem; Erturk, Vakur B.; Chou, Hsi-Tseng (Institute of Electrical and Electronics Engineers (IEEE), 2008-6)
A hybrid method based on the combination of generalized forward backward method (GFBM) and Green's function for the grounded dielectric slab together with the acceleration of the combination via a discrete Fourier transform (DFT) based algorithm is developed for the efficient and accurate analysis of electromagnetic radiation/scattering from electrically large, irregularly contoured two-dimensional arrays consisting of finite number of probe-fed microstrip patches. In this method, unknown current coefficien...
Detection of abnormalities in heart rate using multiple Fourier transforms
Erkuş, Ekin Can; Purutçuoğlu Gazi, Vilda (2019-09-01)
Fourier transform (FT) is one of the transformation techniques to convert the time-domain signal into the frequency-domain signal. Due to its easy usage, it is applied in many engineering approaches where the data are made of periodic components. Electrocardiography (ECG) is an imaging modality which represents the data of electrophysiological activities of heart. ECG data are gathered from the electrodes that are placed on the specific locations on chest, and electrical activities of heart generally produc...
Time series classification with feature covariance matrices
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2018-06-01)
In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices into time series classification problem, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, the time index of the point and cumulative sum up to the point. Extracted feature vectors for the time instances are concatenated t...
Recursive Two-Way Parabolic Equation Approach for Modeling Terrain Effects in Tropospheric Propagation
Ozgun, Ozlem (2009-09-01)
The Fourier split-step method is a one-way marching-type algorithm to efficiently solve the parabolic equation for modeling electromagnetic propagation in troposphere. The main drawback of this method is that it characterizes only forward-propagating waves, and neglects backward-propagating waves, which become important especially in the presence of irregular surfaces. Although ground reflecting boundaries are inherently incorporated into the split-step algorithm, irregular surfaces (such as sharp edges) in...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
V. Purutçuoğlu Gazi and E. C. Erkuş, “Feature extraction of hidden oscillation in ECG data via multiple-FOD method,” 2019, p. 47, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87335.