TY - JOUR
T1 - Continuous wavelet transform and higher-order spectrum
T2 - Combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization
AU - Hadjileontiadis, Leontios J.
N1 - Publisher Copyright:
© 2018 The Author(s) Published by the Royal Society. All rights reserved.
PY - 2018/8/13
Y1 - 2018/8/13
N2 - The combination of the continuous wavelet transform (CWT) with a higher-order spectrum (HOS) merges two worlds into one that conveys information regarding the non-stationarity, non-Gaussianity and nonlinearity of the systems and/or signals under examination. In the current work, the thirdorder spectrum (TOS), which is used to detect the nonlinearity and deviation from Gaussianity of two types of biomedical signals, that is, wheezes and electroencephalogram (EEG), is combined with the CWT to offer a timescale representation of the examined signals. As a result, a CWT/TOS field is formed and a time axis is introduced, creating a time bifrequency domain, which provides a new means for wheeze nonlinear analysis and dynamic EEG-based pain characterization. A detailed description and examples are provided and discussed to showcase the combinatory potential of CWT/TOS in the field of advanced signal processing. This article is part of the theme issue 'Redundancy rules: the continuouswavelet transform comes of age'.
AB - The combination of the continuous wavelet transform (CWT) with a higher-order spectrum (HOS) merges two worlds into one that conveys information regarding the non-stationarity, non-Gaussianity and nonlinearity of the systems and/or signals under examination. In the current work, the thirdorder spectrum (TOS), which is used to detect the nonlinearity and deviation from Gaussianity of two types of biomedical signals, that is, wheezes and electroencephalogram (EEG), is combined with the CWT to offer a timescale representation of the examined signals. As a result, a CWT/TOS field is formed and a time axis is introduced, creating a time bifrequency domain, which provides a new means for wheeze nonlinear analysis and dynamic EEG-based pain characterization. A detailed description and examples are provided and discussed to showcase the combinatory potential of CWT/TOS in the field of advanced signal processing. This article is part of the theme issue 'Redundancy rules: the continuouswavelet transform comes of age'.
KW - breath sounds
KW - continuous wavelet transform
KW - electroencephalogram
KW - higher-order spectrum
KW - pain characterization
KW - wavelet bispectrum
UR - http://www.scopus.com/inward/record.url?scp=85050366984&partnerID=8YFLogxK
U2 - 10.1098/rsta.2017.0249
DO - 10.1098/rsta.2017.0249
M3 - Review article
C2 - 29986918
AN - SCOPUS:85050366984
SN - 1364-503X
VL - 376
JO - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2126
M1 - 20170249
ER -