Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals
We investigated change point detection (CPD) in time series composed of harmonic functions driven by Gaussian noise (in EEGs, in particular) and proposed a method of moving average filters in conjunction with wavelet transform. Numerical simulations showed that CPD runs over 90% within the frequency...
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Veröffentlicht in: | Neurophysiology (New York) 2019-01, Vol.51 (1), p.2-8 |
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description | We investigated change point detection (CPD) in time series composed of harmonic functions driven by Gaussian noise (in EEGs, in particular) and proposed a method of moving average filters in conjunction with wavelet transform. Numerical simulations showed that CPD runs over 90% within the frequency band |
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Numerical simulations showed that CPD runs over 90% within the frequency band <40 Hz. This means that detection of structural change points is almost guaranteed in the respective cases. The mean absolute error (MAE) as a measure of performance of the method was below 5%. The method is rather robust against noise. It has been demonstrated that CPD is possible at the noise amplitude exceeding 25% of the amplitude of harmonic functions. In application of the proposed method on the signals, CPD appeared in 74% of the analyzed EEGs.</description><identifier>ISSN: 0090-2977</identifier><identifier>EISSN: 1573-9007</identifier><identifier>DOI: 10.1007/s11062-019-09783-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Banks (Finance) ; Biomedical and Life Sciences ; Biomedicine ; EEG ; Electroencephalography ; Filters ; Neurophysiology ; Neurosciences ; Noise ; Numerical analysis ; Time series ; Wavelet transforms</subject><ispartof>Neurophysiology (New York), 2019-01, Vol.51 (1), p.2-8</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Neurophysiology is a copyright of Springer, (2019). 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Numerical simulations showed that CPD runs over 90% within the frequency band <40 Hz. This means that detection of structural change points is almost guaranteed in the respective cases. The mean absolute error (MAE) as a measure of performance of the method was below 5%. The method is rather robust against noise. It has been demonstrated that CPD is possible at the noise amplitude exceeding 25% of the amplitude of harmonic functions. In application of the proposed method on the signals, CPD appeared in 74% of the analyzed EEGs.</description><subject>Banks (Finance)</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Filters</subject><subject>Neurophysiology</subject><subject>Neurosciences</subject><subject>Noise</subject><subject>Numerical analysis</subject><subject>Time series</subject><subject>Wavelet transforms</subject><issn>0090-2977</issn><issn>1573-9007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkU9rFDEYh4NYcG39Ap4CnjxMzf9MvC3rthYqlu6Kx5DNZKYps8maZLfdb2_aEaQXJYcXkuf38iMPAO8xOscIyU8ZYyRIg7BqkJItbY6vwAxzSRtVn1-DGUIKNURJ-Qa8zfkeISRaxWfg4YsrzhYfA4w9XNyZMDh4E30oGfoA137r4Mol7zJ88OUOfosHHwY4P7hkKnnhx-JShiZ08Kc5uNEVuE4m5D6m7Wc43-1Gb83z-hLhcnkJV34IZsxn4KSvw737M0_Bj4vlevG1uf5-ebWYXzeWEVQaRTBplcLGkZbWxqaTEkvc8U3LGOJYUG5IzwyRWGws7SQTVmyY5W1HOyMpPQUfpr27FH_tXS76Pu7TUwNNCOGKCYHlfyjMBeNIVep8ogYzOu1DH0sytp7Obb2NwfW-3s95_WUscMtq4OOLQGWKeyyD2eesr1a3L1kysTbFnJPr9S75rUlHjZF-cqwnx7o61s-O9bGG6BTKFa7m0t_e_0j9BrKypxA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Kekovic, G</creator><creator>Sekulic, S</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7TK</scope><scope>K9.</scope><scope>NAPCQ</scope></search><sort><creationdate>20190101</creationdate><title>Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals</title><author>Kekovic, G ; Sekulic, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-92128991ae283689ad77171d5b844051635a2f4a2716bc3d746c6b4c58d3da733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Banks (Finance)</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Filters</topic><topic>Neurophysiology</topic><topic>Neurosciences</topic><topic>Noise</topic><topic>Numerical analysis</topic><topic>Time series</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kekovic, G</creatorcontrib><creatorcontrib>Sekulic, S</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><jtitle>Neurophysiology (New York)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kekovic, G</au><au>Sekulic, S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals</atitle><jtitle>Neurophysiology (New York)</jtitle><stitle>Neurophysiology</stitle><date>2019-01-01</date><risdate>2019</risdate><volume>51</volume><issue>1</issue><spage>2</spage><epage>8</epage><pages>2-8</pages><issn>0090-2977</issn><eissn>1573-9007</eissn><abstract>We investigated change point detection (CPD) in time series composed of harmonic functions driven by Gaussian noise (in EEGs, in particular) and proposed a method of moving average filters in conjunction with wavelet transform. 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subjects | Banks (Finance) Biomedical and Life Sciences Biomedicine EEG Electroencephalography Filters Neurophysiology Neurosciences Noise Numerical analysis Time series Wavelet transforms |
title | Detection of Change Points in Time Series with Moving Average Filters and Wavelet Transform: Application to EEG Signals |
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