@phdthesis{c70ed88efb954aa59476c5e791a83a68,
title = "Statistical inference and time-frequency estimation for non-stationary signal classification",
abstract = "This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. In all the contributions, an example of a biomedical application of the proposed method is provided, either to electroencephalography (EEG) data or to Heart Rate Variability (HRV) data. Four manuscripts are included in this Ph.D. thesis.",
keywords = "Non-stationary processes, stochastic modeling, inference, spectral analysis, time-frequency analysis, classification, biomedical applications, deep learning",
author = "Rachele Anderson",
note = "Defence details Date: 2019-10-04 Time: 09:15 Place: Lecture hall MH:R, Matematikcentrum, S{\"o}lvegatan 18A, Lund External reviewer(s) Name: Baxevani, Anastassia Title: Docent Affiliation: University of Cyprus, Nicosia, Cyprus ---",
year = "2019",
month = sep,
day = "9",
language = "English",
isbn = "978-91-7895-274-8",
series = "Doctoral Theses in Mathematical Sciences",
publisher = "Media-Tryck, Lund University, Sweden",
number = "5",
school = "Mathematical Statistics",
}