Title
Assessing the similarity between time series using a Wavelet transform: Application and interpretability aspects
Abstract
This work presents a simple and interpretable measure to evaluate the similarity between biosignal time series. Combining the Haar wavelet with the Karhunen Loève transforms, the proposed similarity measure is particularly appropriated to deal with noisy signals, with signals that are not time aligned as well as to recognize similar trends. When applied to an indexing scheme, an iterative formulation enables a very efficient computational implementation. Experimental and simulation results using blood pressure signals collected by a telemonitoring platform (TEN-HMS) show the effectiveness of the proposed scheme.
Year
DOI
Venue
2014
10.1109/BHI.2014.6864448
Biomedical and Health Informatics
Keywords
Field
DocType
Haar transforms,blood pressure measurement,iterative methods,medical signal processing,patient monitoring,telemedicine,time series,wavelet transforms,Haar wavelet,Karhunen Loève transforms,TEN-HMS,biosignal time series,blood pressure signals,computational implementation,indexing scheme,interpretability aspects,interpretable measure,iterative formulation,noisy signals,telemonitoring platform,wavelet transform
Data mining,Similarity measure,Second-generation wavelet transform,Discrete wavelet transform,Haar wavelet,Stationary wavelet transform,Wavelet packet decomposition,Mathematics,Wavelet,Wavelet transform
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
T Rocha195.15
S. Paredes22610.61
Paulo Carvalho325047.68
J Henriques43314.56