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Article: Multiscale characterization of chronobiological signals based on the discrete wavelet transform

TitleMultiscale characterization of chronobiological signals based on the discrete wavelet transform
Authors
KeywordsCharacterization
Chronobiological signals
Tree structure
Wavelet maxima
Wavelet transform
Zero-crossings
Issue Date2000
PublisherIEEE.
Citation
IEEE Transactions On Biomedical Engineering, 2000, v. 47 n. 1, p. 88-95 How to Cite?
AbstractTo compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis. | To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this next approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.
Persistent Identifierhttp://hdl.handle.net/10722/42846
ISSN
2015 Impact Factor: 2.468
2015 SCImago Journal Rankings: 1.201
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorWu, BMen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorPoon, PWFen_HK
dc.contributor.authorPoon, AMSen_HK
dc.date.accessioned2007-03-23T04:33:18Z-
dc.date.available2007-03-23T04:33:18Z-
dc.date.issued2000en_HK
dc.identifier.citationIEEE Transactions On Biomedical Engineering, 2000, v. 47 n. 1, p. 88-95en_HK
dc.identifier.issn0018-9294en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42846-
dc.description.abstractTo compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis. | To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this next approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.en_HK
dc.format.extent163604 bytes-
dc.format.extent26624 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Biomedical Engineeringen_HK
dc.rights©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCharacterizationen_HK
dc.subjectChronobiological signalsen_HK
dc.subjectTree structureen_HK
dc.subjectWavelet maximaen_HK
dc.subjectWavelet transformen_HK
dc.subjectZero-crossingsen_HK
dc.titleMultiscale characterization of chronobiological signals based on the discrete wavelet transformen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9294&volume=47&issue=1&spage=88&epage=95&date=2000&atitle=Multiscale+characterization+of+chronobiological+signals+based+on+the+discrete+wavelet+transformen_HK
dc.identifier.emailPoon, AMS: amspoon@hkucc.hku.hken_HK
dc.identifier.emailChan, FHY: fhychan@hkueee.hku.hk-
dc.identifier.emailWu, B: bmwu@eee.hku.hk-
dc.identifier.emailLam, FK: fklam@hkueee.hku.hk-
dc.identifier.authorityPoon, AMS=rp00354en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/10.817623en_HK
dc.identifier.pmid10646283-
dc.identifier.scopuseid_2-s2.0-0033958279en_HK
dc.identifier.hkuros53264-
dc.identifier.hkuros113191-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033958279&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume47en_HK
dc.identifier.issue1en_HK
dc.identifier.spage88en_HK
dc.identifier.epage95en_HK
dc.identifier.isiWOS:000084718000014-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, FHY=7202586429en_HK
dc.identifier.scopusauthoridWu, BM=16685571700en_HK
dc.identifier.scopusauthoridLam, FK=7102075939en_HK
dc.identifier.scopusauthoridPoon, PWF=24322414600en_HK
dc.identifier.scopusauthoridPoon, AMS=7103068868en_HK

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