Time-frequency methodologies in neurosciences

Boashash, B., Stevenson, N. J., Rankine, L. J., Stevenson, N. J., Azemi, G., Sejdic, E., Aviyente, S., Akan, A., Mert, A., Dong, S., Omidvarnia, A., Zarjam, P., O'Toole, J. M. and Colditz, P. (2016). Time-frequency methodologies in neurosciences. In Time-frequency signal analysis and processing: a comprehensive reference Second edition ed. (pp. 915-966) Amsterdam, Netherlands: Academic Press. doi:10.1016/B978-0-12-398499-9.00016-9

Author Boashash, B.
Stevenson, N. J.
Rankine, L. J.
Stevenson, N. J.
Azemi, G.
Sejdic, E.
Aviyente, S.
Akan, A.
Mert, A.
Dong, S.
Omidvarnia, A.
Zarjam, P.
O'Toole, J. M.
Colditz, P.
Title of chapter Time-frequency methodologies in neurosciences
Title of book Time-frequency signal analysis and processing: a comprehensive reference
Place of Publication Amsterdam, Netherlands
Publisher Academic Press
Publication Year 2016
Sub-type Research book chapter (original research)
DOI 10.1016/B978-0-12-398499-9.00016-9
Open Access Status Not yet assessed
Series EURASIP and Academic Press series in signal and image processing
Edition Second edition
ISBN 9780123984999
Chapter number 16
Start page 915
End page 966
Total pages 52
Total chapters 18
Language eng
Formatted Abstract/Summary
This chapter presents a number of time-frequency (t, f) techniques that can provide advanced solutions to several problems in neurosciences with focus on the monitoring of brain abnormalities using the EEG (t, f) characteristics as a diagnosis and prognosis tool. The methods presented illustrate the improved performance obtained by using a time-frequency approach to process EEG data, including a focus on detecting abnormalities in sick newborns in a Neonatal Intensive Care Unit (NICU) as well as mental health issues in elderlies. The material includes methods for the assessment of newborn EEG and ECG abnormalities using a time-frequency identification approach (Section 16.1); TF modeling of nonstationary signals with illustration on newborn EEGs (Section 16.2); time-frequency features for nonstationary signal classification with illustration on newborn EEG burst-suppression detection (Section 16.3); timevarying analysis of brain networks using the EEG for the analysis and detection of Alzheimer disease (Section 16.4); EEG time-frequency analysis and noise reduction using empirical mode decomposition (Section 16.5). Finally the chapter concludes with a discussion on the perspectives of using advanced (t, f) methods for medical diagnosis and prognosis in other areas of neurosciences (Section 16.6).
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Book Chapter
Collections: UQ Centre for Clinical Research Publications
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