Detection of dynamic rhythms of electroencephalography by using wavelet packets decomposition
Wavelet packet decomposition is used to investigate. the time-varying characteristics of clinical EEG signals. On the basis of the nonstationary nature of clinical EEG rhythms, wavelet packet analysis is employed for designing filters with different frequency characteristics to detect 4 kinds of EEG...
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| creator | Minfen Shen Lisha Sun Chan, F.H.Y. |
| description | Wavelet packet decomposition is used to investigate. the time-varying characteristics of clinical EEG signals. On the basis of the nonstationary nature of clinical EEG rhythms, wavelet packet analysis is employed for designing filters with different frequency characteristics to detect 4 kinds of EEG rhythms. The coefficients of wavelet transformation corresponding to the rhythms are used to form the dynamic brain electrical activity mapping (DBEAM). In order to understand the dynamic rhythms of the EEG, some clinical EEG are analyzed and compared. It is indicated from the experimental results that the dynamic characteristics of clinical brain electrical activities can be provided in terms of wavelet packet decomposition. |
| doi_str_mv | 10.1109/IEMBS.2001.1020588 |
| format | Conference Proceeding |
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It is indicated from the experimental results that the dynamic characteristics of clinical brain electrical activities can be provided in terms of wavelet packet decomposition.</description><identifier>ISSN: 1094-687X</identifier><identifier>ISBN: 9780780372115</identifier><identifier>ISBN: 0780372115</identifier><identifier>EISSN: 1558-4615</identifier><identifier>DOI: 10.1109/IEMBS.2001.1020588</identifier><language>eng</language><publisher>IEEE</publisher><subject>Brain ; Electroencephalography ; Frequency ; Rhythm ; Signal analysis ; Signal processing ; Spectral analysis ; Transient analysis ; Wavelet analysis ; Wavelet packets</subject><ispartof>2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, Vol.2, p.1865-1868 vol.2</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1020588$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,4036,4037,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1020588$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Minfen Shen</creatorcontrib><creatorcontrib>Lisha Sun</creatorcontrib><creatorcontrib>Chan, F.H.Y.</creatorcontrib><title>Detection of dynamic rhythms of electroencephalography by using wavelet packets decomposition</title><title>2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>IEMBS</addtitle><description>Wavelet packet decomposition is used to investigate. the time-varying characteristics of clinical EEG signals. On the basis of the nonstationary nature of clinical EEG rhythms, wavelet packet analysis is employed for designing filters with different frequency characteristics to detect 4 kinds of EEG rhythms. The coefficients of wavelet transformation corresponding to the rhythms are used to form the dynamic brain electrical activity mapping (DBEAM). In order to understand the dynamic rhythms of the EEG, some clinical EEG are analyzed and compared. It is indicated from the experimental results that the dynamic characteristics of clinical brain electrical activities can be provided in terms of wavelet packet decomposition.</description><subject>Brain</subject><subject>Electroencephalography</subject><subject>Frequency</subject><subject>Rhythm</subject><subject>Signal analysis</subject><subject>Signal processing</subject><subject>Spectral analysis</subject><subject>Transient analysis</subject><subject>Wavelet analysis</subject><subject>Wavelet packets</subject><issn>1094-687X</issn><issn>1558-4615</issn><isbn>9780780372115</isbn><isbn>0780372115</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9j8FOwzAQRC0oEhXND8DFP5B0ncSNfQWK2gMnOHBBlXG3jSGJLduA_Pe4Us-MRlpp3milIeSWQcUYyOV2_Xz_UtUArGJQAxfigswZ56JsV4xfkkJ2ArKbrmaMzzID2ZYr0b1dkyKET8hqZNvIek7eHzGijsZO1B7oPk1qNJr6PsV-DKcIh4y9xUmj69Vgj165PtGPRL-DmY70V_3kSqRO6S-Mge5R29HZYE4_F-TqoIaAxfnekLun9evDpjSIuHPejMqn3XlE8z_9A6e5SaE</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Minfen Shen</creator><creator>Lisha Sun</creator><creator>Chan, F.H.Y.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2001</creationdate><title>Detection of dynamic rhythms of electroencephalography by using wavelet packets decomposition</title><author>Minfen Shen ; Lisha Sun ; Chan, F.H.Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_10205883</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Brain</topic><topic>Electroencephalography</topic><topic>Frequency</topic><topic>Rhythm</topic><topic>Signal analysis</topic><topic>Signal processing</topic><topic>Spectral analysis</topic><topic>Transient analysis</topic><topic>Wavelet analysis</topic><topic>Wavelet packets</topic><toplevel>online_resources</toplevel><creatorcontrib>Minfen Shen</creatorcontrib><creatorcontrib>Lisha Sun</creatorcontrib><creatorcontrib>Chan, F.H.Y.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Minfen Shen</au><au>Lisha Sun</au><au>Chan, F.H.Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Detection of dynamic rhythms of electroencephalography by using wavelet packets decomposition</atitle><btitle>2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>IEMBS</stitle><date>2001</date><risdate>2001</risdate><volume>2</volume><spage>1865</spage><epage>1868 vol.2</epage><pages>1865-1868 vol.2</pages><issn>1094-687X</issn><eissn>1558-4615</eissn><isbn>9780780372115</isbn><isbn>0780372115</isbn><abstract>Wavelet packet decomposition is used to investigate. the time-varying characteristics of clinical EEG signals. On the basis of the nonstationary nature of clinical EEG rhythms, wavelet packet analysis is employed for designing filters with different frequency characteristics to detect 4 kinds of EEG rhythms. The coefficients of wavelet transformation corresponding to the rhythms are used to form the dynamic brain electrical activity mapping (DBEAM). In order to understand the dynamic rhythms of the EEG, some clinical EEG are analyzed and compared. It is indicated from the experimental results that the dynamic characteristics of clinical brain electrical activities can be provided in terms of wavelet packet decomposition.</abstract><pub>IEEE</pub><doi>10.1109/IEMBS.2001.1020588</doi></addata></record> |
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| ispartof | 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, Vol.2, p.1865-1868 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_1020588 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Brain Electroencephalography Frequency Rhythm Signal analysis Signal processing Spectral analysis Transient analysis Wavelet analysis Wavelet packets |
| title | Detection of dynamic rhythms of electroencephalography by using wavelet packets decomposition |
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