TY - JOUR

T1 - An Iterative Warping and Clustering Algorithm to Estimate Multiple Wave-Shape Functions From a Nonstationary Oscillatory Signal

AU - Colominas, Marcelo A.

AU - Wu, Hau Tieng

N1 - Publisher Copyright:
© 1991-2012 IEEE.

PY - 2023

Y1 - 2023

N2 - Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, s1,sK, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate s1,sK from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.

AB - Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, s1,sK, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate s1,sK from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.

KW - biomedical signals

KW - instan-taneous frequency

KW - signal modeling

KW - Wave-shape functions

UR - http://www.scopus.com/inward/record.url?scp=85151260074&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85151260074&partnerID=8YFLogxK

U2 - 10.1109/TSP.2023.3252883

DO - 10.1109/TSP.2023.3252883

M3 - Article

AN - SCOPUS:85151260074

SN - 1053-587X

VL - 71

SP - 701

EP - 712

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

ER -