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   -> Volume 6, Issue 8


Preprint: Analysis of a Physiological Time Series using Wavelets
 
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Alejandra Figliola (figliola@ulises.ic.fcen.uba.ar)
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PostPosted: Thu Jul 31, 1997 8:58 pm    
Subject: Preprint: Analysis of a Physiological Time Series using Wavelets
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#4 Preprint: Analysis of a Physiological Time Series using Wavelets

Title: Analysis of a Physiological Time Series using Wavelet
Transform.

Authors: Alejandra Figliola and Eduardo Serrano

Instituto de Calculo, Facultad de Ciencias
Exactas y Naturales.(UBA)\Pab.I Ciudad Universitaria.
(1428) Buenos Aires. Argentina.
e-mails: figliola@ulises.ic.fcen.uba.ar and eduser@mate.uba.ar}}

Abstract

A new method based on the Wavelet Transform Theory, to quantify and
visualize the order of a system, is present. It is applied to
analyzing the data set of the '1991 Santa Fe Time Series Prediction
and Analysis Competition', that consists of 4 hours and 43 minutes of
simultaneous heart rate, variation chest volume, blood oxygen
saturation, and EEG sleep stage, of a patient with apneas,
i. e. periods in which he takes a few quick breaths and then he stops
breathing, during the sleeping period. The phenomenon is non
stationary, because it not conserve in time the value of its mean
value and variance, and the three series are contaminated with
noise. Apneas, intermittent apneas and regular behaviors alternate
during the process and the data values appear in a complex
structure. Wavelet techniques are very powerful to detect changes in
the signals. Also, since the wavelet coefficients it is possible to
define the Information Cost Function (ICF) which is capable to
evaluate the order (or disorder) of a dynamic al system, also when its
time series is non stationary and noised.

Published in: IEEE Engineering in Med. & Biol., 16, N 3,
May/June 1997
All times are GMT + 1 Hour
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