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- W2536302544 abstract "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)." @default.
- W2536302544 created "2016-10-28" @default.
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- W2536302544 date "2016-01-01" @default.
- W2536302544 modified "2023-10-17" @default.
- W2536302544 title "Time-Frequency Methodologies in Neurosciences" @default.
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- W2536302544 doi "https://doi.org/10.1016/b978-0-12-398499-9.00016-9" @default.
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